{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "007cb85a",
   "metadata": {},
   "source": [
    "### 2.建模"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "803ed6d1",
   "metadata": {},
   "source": [
    "- 5折交叉验证建模"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aa740489",
   "metadata": {},
   "source": [
    "#### 数据准备"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "df71e579",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from tqdm import tqdm\n",
    "import numpy as np\n",
    "from sklearn.model_selection import cross_val_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "81e8bbe3",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_roc_auc(model,test_X,test_label_list):\n",
    "    from sklearn.metrics import roc_curve, auc\n",
    "    import matplotlib.pyplot as plt\n",
    "    predict_pro_list = [i[1] for i in model.predict_proba(test_X)]  # 1代表高\n",
    "    fpr, tpr, thersholds = roc_curve(test_label_list, predict_pro_list, pos_label=1)\n",
    "    roc_auc = auc(fpr, tpr)\n",
    "    plt.plot(fpr, tpr, 'k--', label='ROC (area = {0:.2f})'.format(roc_auc), lw=2)\n",
    " \n",
    "    plt.xlim([-0.05, 1.05])  # 设置x、y轴的上下限，以免和边缘重合，更好的观察图像的整体\n",
    "    plt.ylim([-0.05, 1.05])\n",
    "    plt.xlabel('False Positive Rate')\n",
    "    plt.ylabel('True Positive Rate')  # 可以使用中文，但需要导入一些库即字体\n",
    "    plt.title('ROC Curve')\n",
    "    plt.legend(loc=\"lower right\")\n",
    "    plt.show()\n",
    "    return predict_pro_list, roc_auc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "e7c19efe",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.metrics import precision_recall_fscore_support\n",
    "\n",
    "def eval_model(test_label_list, predict_label_list, className_list):\n",
    "    p, r, f1, s = precision_recall_fscore_support(test_label_list, predict_label_list)\n",
    "    total_p = np.average(p, weights=s)\n",
    "    total_r = np.average(r, weights=s)\n",
    "    total_f1 = np.average(f1, weights=s)\n",
    "    total_s = np.sum(s)\n",
    "    res1 = pd.DataFrame({\n",
    "        u'Label': className_list,\n",
    "        u'Precision': p,\n",
    "        u'Recall': r,\n",
    "        u'F1': f1,\n",
    "        u'Support': s\n",
    "    })\n",
    "    res2 = pd.DataFrame({\n",
    "        u'Label': ['总体'],\n",
    "        u'Precision': [total_p],\n",
    "        u'Recall': [total_r],\n",
    "        u'F1': [total_f1],\n",
    "        u'Support': [total_s]\n",
    "    })\n",
    "    res2.index = [2]\n",
    "    res = pd.concat([res1, res2])\n",
    "    df = res[['Label', 'Precision', 'Recall', 'F1', 'Support']]\n",
    "#     df.to_csv(path)\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "e76dadba",
   "metadata": {},
   "outputs": [],
   "source": [
    "train = pd.read_excel(r'D:\\jupyter\\2023.6博士期间\\2024.2.19XY的work\\数据\\train.xlsx')\n",
    "test = pd.read_excel(r'D:\\jupyter\\2023.6博士期间\\2024.2.19XY的work\\数据\\test.xlsx')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "d935c9e8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train (2942, 22)\n",
      "test (783, 22)\n"
     ]
    }
   ],
   "source": [
    "print('train',train.shape)\n",
    "print('test',test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "c13b9d2f",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>X1</th>\n",
       "      <th>X2</th>\n",
       "      <th>X3</th>\n",
       "      <th>X4</th>\n",
       "      <th>X5</th>\n",
       "      <th>X6</th>\n",
       "      <th>X7</th>\n",
       "      <th>X8</th>\n",
       "      <th>X9</th>\n",
       "      <th>X10</th>\n",
       "      <th>...</th>\n",
       "      <th>X13</th>\n",
       "      <th>X14</th>\n",
       "      <th>X15</th>\n",
       "      <th>X16</th>\n",
       "      <th>X17</th>\n",
       "      <th>X18</th>\n",
       "      <th>X19</th>\n",
       "      <th>X20</th>\n",
       "      <th>X21</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.090870</td>\n",
       "      <td>-0.414352</td>\n",
       "      <td>-0.315186</td>\n",
       "      <td>-0.640501</td>\n",
       "      <td>-0.570438</td>\n",
       "      <td>-0.325930</td>\n",
       "      <td>-0.089354</td>\n",
       "      <td>-0.216582</td>\n",
       "      <td>-0.166966</td>\n",
       "      <td>-0.309157</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.194138</td>\n",
       "      <td>-0.299418</td>\n",
       "      <td>0.979306</td>\n",
       "      <td>-0.146538</td>\n",
       "      <td>-0.141895</td>\n",
       "      <td>-0.556419</td>\n",
       "      <td>-0.288801</td>\n",
       "      <td>-0.404947</td>\n",
       "      <td>-0.101543</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.114564</td>\n",
       "      <td>5.218307</td>\n",
       "      <td>0.179099</td>\n",
       "      <td>-0.066085</td>\n",
       "      <td>-0.570438</td>\n",
       "      <td>10.430409</td>\n",
       "      <td>3.651422</td>\n",
       "      <td>4.064608</td>\n",
       "      <td>3.763123</td>\n",
       "      <td>-0.219984</td>\n",
       "      <td>...</td>\n",
       "      <td>4.070741</td>\n",
       "      <td>1.992031</td>\n",
       "      <td>-0.558939</td>\n",
       "      <td>1.506001</td>\n",
       "      <td>-0.141895</td>\n",
       "      <td>-0.556419</td>\n",
       "      <td>0.526311</td>\n",
       "      <td>1.254308</td>\n",
       "      <td>-0.101543</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.176973</td>\n",
       "      <td>-0.414352</td>\n",
       "      <td>-0.315186</td>\n",
       "      <td>0.346217</td>\n",
       "      <td>1.214261</td>\n",
       "      <td>-0.101422</td>\n",
       "      <td>-0.089354</td>\n",
       "      <td>-0.086694</td>\n",
       "      <td>-0.285430</td>\n",
       "      <td>-0.310106</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.128906</td>\n",
       "      <td>-0.891851</td>\n",
       "      <td>-0.956568</td>\n",
       "      <td>1.518108</td>\n",
       "      <td>-0.141895</td>\n",
       "      <td>1.292904</td>\n",
       "      <td>0.719790</td>\n",
       "      <td>2.445281</td>\n",
       "      <td>-0.101543</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.046847</td>\n",
       "      <td>-0.414352</td>\n",
       "      <td>-0.315186</td>\n",
       "      <td>-0.640501</td>\n",
       "      <td>-0.570438</td>\n",
       "      <td>-0.325930</td>\n",
       "      <td>0.092159</td>\n",
       "      <td>-0.216582</td>\n",
       "      <td>-0.197428</td>\n",
       "      <td>-0.310106</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.194138</td>\n",
       "      <td>-0.086879</td>\n",
       "      <td>0.106243</td>\n",
       "      <td>-0.960319</td>\n",
       "      <td>-0.141895</td>\n",
       "      <td>0.249005</td>\n",
       "      <td>-0.144716</td>\n",
       "      <td>-0.404947</td>\n",
       "      <td>-0.101543</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.108843</td>\n",
       "      <td>-0.414352</td>\n",
       "      <td>-0.315186</td>\n",
       "      <td>-0.640501</td>\n",
       "      <td>-0.570438</td>\n",
       "      <td>-0.160637</td>\n",
       "      <td>-0.032605</td>\n",
       "      <td>-0.105131</td>\n",
       "      <td>-0.286521</td>\n",
       "      <td>-0.310106</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.119507</td>\n",
       "      <td>-0.152474</td>\n",
       "      <td>-0.187675</td>\n",
       "      <td>-0.840624</td>\n",
       "      <td>-0.141895</td>\n",
       "      <td>1.249215</td>\n",
       "      <td>-0.144716</td>\n",
       "      <td>-0.404947</td>\n",
       "      <td>-0.101543</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>2937</th>\n",
       "      <td>-0.176973</td>\n",
       "      <td>-0.235330</td>\n",
       "      <td>-0.315186</td>\n",
       "      <td>0.171157</td>\n",
       "      <td>-0.570438</td>\n",
       "      <td>-0.325930</td>\n",
       "      <td>0.185725</td>\n",
       "      <td>-0.216582</td>\n",
       "      <td>-0.282063</td>\n",
       "      <td>-0.310106</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.194138</td>\n",
       "      <td>8.371402</td>\n",
       "      <td>-0.452577</td>\n",
       "      <td>-0.344108</td>\n",
       "      <td>-0.141895</td>\n",
       "      <td>-0.195510</td>\n",
       "      <td>0.670980</td>\n",
       "      <td>2.654748</td>\n",
       "      <td>-0.101543</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2938</th>\n",
       "      <td>-0.176973</td>\n",
       "      <td>0.594697</td>\n",
       "      <td>-0.315186</td>\n",
       "      <td>-0.640501</td>\n",
       "      <td>-0.570438</td>\n",
       "      <td>-0.325930</td>\n",
       "      <td>-0.569770</td>\n",
       "      <td>-0.216582</td>\n",
       "      <td>-0.425902</td>\n",
       "      <td>-0.310106</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.194138</td>\n",
       "      <td>-0.553318</td>\n",
       "      <td>-0.699354</td>\n",
       "      <td>-0.743298</td>\n",
       "      <td>-0.141895</td>\n",
       "      <td>-0.556419</td>\n",
       "      <td>-0.288801</td>\n",
       "      <td>-0.404947</td>\n",
       "      <td>-0.101543</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2939</th>\n",
       "      <td>0.125998</td>\n",
       "      <td>3.621842</td>\n",
       "      <td>0.276274</td>\n",
       "      <td>1.183079</td>\n",
       "      <td>1.214261</td>\n",
       "      <td>1.762834</td>\n",
       "      <td>0.693570</td>\n",
       "      <td>0.585801</td>\n",
       "      <td>-0.136688</td>\n",
       "      <td>0.251222</td>\n",
       "      <td>...</td>\n",
       "      <td>0.574450</td>\n",
       "      <td>1.070736</td>\n",
       "      <td>0.593058</td>\n",
       "      <td>0.503062</td>\n",
       "      <td>-0.141895</td>\n",
       "      <td>-0.556419</td>\n",
       "      <td>0.292931</td>\n",
       "      <td>-0.006986</td>\n",
       "      <td>-0.101543</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2940</th>\n",
       "      <td>0.339642</td>\n",
       "      <td>-0.414352</td>\n",
       "      <td>-0.315186</td>\n",
       "      <td>-0.640501</td>\n",
       "      <td>-0.570438</td>\n",
       "      <td>-0.325930</td>\n",
       "      <td>-0.449666</td>\n",
       "      <td>-0.216582</td>\n",
       "      <td>-0.138974</td>\n",
       "      <td>-0.310106</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.194138</td>\n",
       "      <td>-0.807218</td>\n",
       "      <td>1.493734</td>\n",
       "      <td>0.387405</td>\n",
       "      <td>-0.141895</td>\n",
       "      <td>1.385371</td>\n",
       "      <td>-0.144716</td>\n",
       "      <td>-0.404947</td>\n",
       "      <td>-0.101543</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2941</th>\n",
       "      <td>-0.176973</td>\n",
       "      <td>-0.414352</td>\n",
       "      <td>-0.315186</td>\n",
       "      <td>-0.640501</td>\n",
       "      <td>-0.570438</td>\n",
       "      <td>-0.325930</td>\n",
       "      <td>-0.569770</td>\n",
       "      <td>-0.216582</td>\n",
       "      <td>-0.425902</td>\n",
       "      <td>-0.310106</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.194138</td>\n",
       "      <td>-0.214785</td>\n",
       "      <td>-0.056319</td>\n",
       "      <td>-1.120199</td>\n",
       "      <td>-0.141895</td>\n",
       "      <td>-0.556419</td>\n",
       "      <td>-0.144716</td>\n",
       "      <td>-0.404947</td>\n",
       "      <td>-0.101543</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2942 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            X1        X2        X3        X4        X5         X6        X7  \\\n",
       "0    -0.090870 -0.414352 -0.315186 -0.640501 -0.570438  -0.325930 -0.089354   \n",
       "1     1.114564  5.218307  0.179099 -0.066085 -0.570438  10.430409  3.651422   \n",
       "2    -0.176973 -0.414352 -0.315186  0.346217  1.214261  -0.101422 -0.089354   \n",
       "3    -0.046847 -0.414352 -0.315186 -0.640501 -0.570438  -0.325930  0.092159   \n",
       "4    -0.108843 -0.414352 -0.315186 -0.640501 -0.570438  -0.160637 -0.032605   \n",
       "...        ...       ...       ...       ...       ...        ...       ...   \n",
       "2937 -0.176973 -0.235330 -0.315186  0.171157 -0.570438  -0.325930  0.185725   \n",
       "2938 -0.176973  0.594697 -0.315186 -0.640501 -0.570438  -0.325930 -0.569770   \n",
       "2939  0.125998  3.621842  0.276274  1.183079  1.214261   1.762834  0.693570   \n",
       "2940  0.339642 -0.414352 -0.315186 -0.640501 -0.570438  -0.325930 -0.449666   \n",
       "2941 -0.176973 -0.414352 -0.315186 -0.640501 -0.570438  -0.325930 -0.569770   \n",
       "\n",
       "            X8        X9       X10  ...       X13       X14       X15  \\\n",
       "0    -0.216582 -0.166966 -0.309157  ... -0.194138 -0.299418  0.979306   \n",
       "1     4.064608  3.763123 -0.219984  ...  4.070741  1.992031 -0.558939   \n",
       "2    -0.086694 -0.285430 -0.310106  ... -0.128906 -0.891851 -0.956568   \n",
       "3    -0.216582 -0.197428 -0.310106  ... -0.194138 -0.086879  0.106243   \n",
       "4    -0.105131 -0.286521 -0.310106  ... -0.119507 -0.152474 -0.187675   \n",
       "...        ...       ...       ...  ...       ...       ...       ...   \n",
       "2937 -0.216582 -0.282063 -0.310106  ... -0.194138  8.371402 -0.452577   \n",
       "2938 -0.216582 -0.425902 -0.310106  ... -0.194138 -0.553318 -0.699354   \n",
       "2939  0.585801 -0.136688  0.251222  ...  0.574450  1.070736  0.593058   \n",
       "2940 -0.216582 -0.138974 -0.310106  ... -0.194138 -0.807218  1.493734   \n",
       "2941 -0.216582 -0.425902 -0.310106  ... -0.194138 -0.214785 -0.056319   \n",
       "\n",
       "           X16       X17       X18       X19       X20       X21  target  \n",
       "0    -0.146538 -0.141895 -0.556419 -0.288801 -0.404947 -0.101543       0  \n",
       "1     1.506001 -0.141895 -0.556419  0.526311  1.254308 -0.101543       1  \n",
       "2     1.518108 -0.141895  1.292904  0.719790  2.445281 -0.101543       0  \n",
       "3    -0.960319 -0.141895  0.249005 -0.144716 -0.404947 -0.101543       1  \n",
       "4    -0.840624 -0.141895  1.249215 -0.144716 -0.404947 -0.101543       1  \n",
       "...        ...       ...       ...       ...       ...       ...     ...  \n",
       "2937 -0.344108 -0.141895 -0.195510  0.670980  2.654748 -0.101543       1  \n",
       "2938 -0.743298 -0.141895 -0.556419 -0.288801 -0.404947 -0.101543       0  \n",
       "2939  0.503062 -0.141895 -0.556419  0.292931 -0.006986 -0.101543       1  \n",
       "2940  0.387405 -0.141895  1.385371 -0.144716 -0.404947 -0.101543       0  \n",
       "2941 -1.120199 -0.141895 -0.556419 -0.144716 -0.404947 -0.101543       0  \n",
       "\n",
       "[2942 rows x 22 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5f91f902",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "7c55ac68",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import LabelEncoder\n",
    "labelEncoder = LabelEncoder()\n",
    "\n",
    "train_y = labelEncoder.fit_transform(train['target'])\n",
    "test_y = labelEncoder.fit_transform(test['target'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "8c2f5120",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_X ,test_X = train.iloc[:,:-1],test.iloc[:,:-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "8bdf100a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2942, 21)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "23dbac69",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>X1</th>\n",
       "      <th>X2</th>\n",
       "      <th>X3</th>\n",
       "      <th>X4</th>\n",
       "      <th>X5</th>\n",
       "      <th>X6</th>\n",
       "      <th>X7</th>\n",
       "      <th>X8</th>\n",
       "      <th>X9</th>\n",
       "      <th>X10</th>\n",
       "      <th>...</th>\n",
       "      <th>X12</th>\n",
       "      <th>X13</th>\n",
       "      <th>X14</th>\n",
       "      <th>X15</th>\n",
       "      <th>X16</th>\n",
       "      <th>X17</th>\n",
       "      <th>X18</th>\n",
       "      <th>X19</th>\n",
       "      <th>X20</th>\n",
       "      <th>X21</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.090870</td>\n",
       "      <td>-0.414352</td>\n",
       "      <td>-0.315186</td>\n",
       "      <td>-0.640501</td>\n",
       "      <td>-0.570438</td>\n",
       "      <td>-0.325930</td>\n",
       "      <td>-0.089354</td>\n",
       "      <td>-0.216582</td>\n",
       "      <td>-0.166966</td>\n",
       "      <td>-0.309157</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.239565</td>\n",
       "      <td>-0.194138</td>\n",
       "      <td>-0.299418</td>\n",
       "      <td>0.979306</td>\n",
       "      <td>-0.146538</td>\n",
       "      <td>-0.141895</td>\n",
       "      <td>-0.556419</td>\n",
       "      <td>-0.288801</td>\n",
       "      <td>-0.404947</td>\n",
       "      <td>-0.101543</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.114564</td>\n",
       "      <td>5.218307</td>\n",
       "      <td>0.179099</td>\n",
       "      <td>-0.066085</td>\n",
       "      <td>-0.570438</td>\n",
       "      <td>10.430409</td>\n",
       "      <td>3.651422</td>\n",
       "      <td>4.064608</td>\n",
       "      <td>3.763123</td>\n",
       "      <td>-0.219984</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.239565</td>\n",
       "      <td>4.070741</td>\n",
       "      <td>1.992031</td>\n",
       "      <td>-0.558939</td>\n",
       "      <td>1.506001</td>\n",
       "      <td>-0.141895</td>\n",
       "      <td>-0.556419</td>\n",
       "      <td>0.526311</td>\n",
       "      <td>1.254308</td>\n",
       "      <td>-0.101543</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.176973</td>\n",
       "      <td>-0.414352</td>\n",
       "      <td>-0.315186</td>\n",
       "      <td>0.346217</td>\n",
       "      <td>1.214261</td>\n",
       "      <td>-0.101422</td>\n",
       "      <td>-0.089354</td>\n",
       "      <td>-0.086694</td>\n",
       "      <td>-0.285430</td>\n",
       "      <td>-0.310106</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.239565</td>\n",
       "      <td>-0.128906</td>\n",
       "      <td>-0.891851</td>\n",
       "      <td>-0.956568</td>\n",
       "      <td>1.518108</td>\n",
       "      <td>-0.141895</td>\n",
       "      <td>1.292904</td>\n",
       "      <td>0.719790</td>\n",
       "      <td>2.445281</td>\n",
       "      <td>-0.101543</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.046847</td>\n",
       "      <td>-0.414352</td>\n",
       "      <td>-0.315186</td>\n",
       "      <td>-0.640501</td>\n",
       "      <td>-0.570438</td>\n",
       "      <td>-0.325930</td>\n",
       "      <td>0.092159</td>\n",
       "      <td>-0.216582</td>\n",
       "      <td>-0.197428</td>\n",
       "      <td>-0.310106</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.239565</td>\n",
       "      <td>-0.194138</td>\n",
       "      <td>-0.086879</td>\n",
       "      <td>0.106243</td>\n",
       "      <td>-0.960319</td>\n",
       "      <td>-0.141895</td>\n",
       "      <td>0.249005</td>\n",
       "      <td>-0.144716</td>\n",
       "      <td>-0.404947</td>\n",
       "      <td>-0.101543</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.108843</td>\n",
       "      <td>-0.414352</td>\n",
       "      <td>-0.315186</td>\n",
       "      <td>-0.640501</td>\n",
       "      <td>-0.570438</td>\n",
       "      <td>-0.160637</td>\n",
       "      <td>-0.032605</td>\n",
       "      <td>-0.105131</td>\n",
       "      <td>-0.286521</td>\n",
       "      <td>-0.310106</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.239565</td>\n",
       "      <td>-0.119507</td>\n",
       "      <td>-0.152474</td>\n",
       "      <td>-0.187675</td>\n",
       "      <td>-0.840624</td>\n",
       "      <td>-0.141895</td>\n",
       "      <td>1.249215</td>\n",
       "      <td>-0.144716</td>\n",
       "      <td>-0.404947</td>\n",
       "      <td>-0.101543</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2937</th>\n",
       "      <td>-0.176973</td>\n",
       "      <td>-0.235330</td>\n",
       "      <td>-0.315186</td>\n",
       "      <td>0.171157</td>\n",
       "      <td>-0.570438</td>\n",
       "      <td>-0.325930</td>\n",
       "      <td>0.185725</td>\n",
       "      <td>-0.216582</td>\n",
       "      <td>-0.282063</td>\n",
       "      <td>-0.310106</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.239565</td>\n",
       "      <td>-0.194138</td>\n",
       "      <td>8.371402</td>\n",
       "      <td>-0.452577</td>\n",
       "      <td>-0.344108</td>\n",
       "      <td>-0.141895</td>\n",
       "      <td>-0.195510</td>\n",
       "      <td>0.670980</td>\n",
       "      <td>2.654748</td>\n",
       "      <td>-0.101543</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2938</th>\n",
       "      <td>-0.176973</td>\n",
       "      <td>0.594697</td>\n",
       "      <td>-0.315186</td>\n",
       "      <td>-0.640501</td>\n",
       "      <td>-0.570438</td>\n",
       "      <td>-0.325930</td>\n",
       "      <td>-0.569770</td>\n",
       "      <td>-0.216582</td>\n",
       "      <td>-0.425902</td>\n",
       "      <td>-0.310106</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.239565</td>\n",
       "      <td>-0.194138</td>\n",
       "      <td>-0.553318</td>\n",
       "      <td>-0.699354</td>\n",
       "      <td>-0.743298</td>\n",
       "      <td>-0.141895</td>\n",
       "      <td>-0.556419</td>\n",
       "      <td>-0.288801</td>\n",
       "      <td>-0.404947</td>\n",
       "      <td>-0.101543</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2939</th>\n",
       "      <td>0.125998</td>\n",
       "      <td>3.621842</td>\n",
       "      <td>0.276274</td>\n",
       "      <td>1.183079</td>\n",
       "      <td>1.214261</td>\n",
       "      <td>1.762834</td>\n",
       "      <td>0.693570</td>\n",
       "      <td>0.585801</td>\n",
       "      <td>-0.136688</td>\n",
       "      <td>0.251222</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.239565</td>\n",
       "      <td>0.574450</td>\n",
       "      <td>1.070736</td>\n",
       "      <td>0.593058</td>\n",
       "      <td>0.503062</td>\n",
       "      <td>-0.141895</td>\n",
       "      <td>-0.556419</td>\n",
       "      <td>0.292931</td>\n",
       "      <td>-0.006986</td>\n",
       "      <td>-0.101543</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2940</th>\n",
       "      <td>0.339642</td>\n",
       "      <td>-0.414352</td>\n",
       "      <td>-0.315186</td>\n",
       "      <td>-0.640501</td>\n",
       "      <td>-0.570438</td>\n",
       "      <td>-0.325930</td>\n",
       "      <td>-0.449666</td>\n",
       "      <td>-0.216582</td>\n",
       "      <td>-0.138974</td>\n",
       "      <td>-0.310106</td>\n",
       "      <td>...</td>\n",
       "      <td>4.174236</td>\n",
       "      <td>-0.194138</td>\n",
       "      <td>-0.807218</td>\n",
       "      <td>1.493734</td>\n",
       "      <td>0.387405</td>\n",
       "      <td>-0.141895</td>\n",
       "      <td>1.385371</td>\n",
       "      <td>-0.144716</td>\n",
       "      <td>-0.404947</td>\n",
       "      <td>-0.101543</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2941</th>\n",
       "      <td>-0.176973</td>\n",
       "      <td>-0.414352</td>\n",
       "      <td>-0.315186</td>\n",
       "      <td>-0.640501</td>\n",
       "      <td>-0.570438</td>\n",
       "      <td>-0.325930</td>\n",
       "      <td>-0.569770</td>\n",
       "      <td>-0.216582</td>\n",
       "      <td>-0.425902</td>\n",
       "      <td>-0.310106</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.239565</td>\n",
       "      <td>-0.194138</td>\n",
       "      <td>-0.214785</td>\n",
       "      <td>-0.056319</td>\n",
       "      <td>-1.120199</td>\n",
       "      <td>-0.141895</td>\n",
       "      <td>-0.556419</td>\n",
       "      <td>-0.144716</td>\n",
       "      <td>-0.404947</td>\n",
       "      <td>-0.101543</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2942 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            X1        X2        X3        X4        X5         X6        X7  \\\n",
       "0    -0.090870 -0.414352 -0.315186 -0.640501 -0.570438  -0.325930 -0.089354   \n",
       "1     1.114564  5.218307  0.179099 -0.066085 -0.570438  10.430409  3.651422   \n",
       "2    -0.176973 -0.414352 -0.315186  0.346217  1.214261  -0.101422 -0.089354   \n",
       "3    -0.046847 -0.414352 -0.315186 -0.640501 -0.570438  -0.325930  0.092159   \n",
       "4    -0.108843 -0.414352 -0.315186 -0.640501 -0.570438  -0.160637 -0.032605   \n",
       "...        ...       ...       ...       ...       ...        ...       ...   \n",
       "2937 -0.176973 -0.235330 -0.315186  0.171157 -0.570438  -0.325930  0.185725   \n",
       "2938 -0.176973  0.594697 -0.315186 -0.640501 -0.570438  -0.325930 -0.569770   \n",
       "2939  0.125998  3.621842  0.276274  1.183079  1.214261   1.762834  0.693570   \n",
       "2940  0.339642 -0.414352 -0.315186 -0.640501 -0.570438  -0.325930 -0.449666   \n",
       "2941 -0.176973 -0.414352 -0.315186 -0.640501 -0.570438  -0.325930 -0.569770   \n",
       "\n",
       "            X8        X9       X10  ...       X12       X13       X14  \\\n",
       "0    -0.216582 -0.166966 -0.309157  ... -0.239565 -0.194138 -0.299418   \n",
       "1     4.064608  3.763123 -0.219984  ... -0.239565  4.070741  1.992031   \n",
       "2    -0.086694 -0.285430 -0.310106  ... -0.239565 -0.128906 -0.891851   \n",
       "3    -0.216582 -0.197428 -0.310106  ... -0.239565 -0.194138 -0.086879   \n",
       "4    -0.105131 -0.286521 -0.310106  ... -0.239565 -0.119507 -0.152474   \n",
       "...        ...       ...       ...  ...       ...       ...       ...   \n",
       "2937 -0.216582 -0.282063 -0.310106  ... -0.239565 -0.194138  8.371402   \n",
       "2938 -0.216582 -0.425902 -0.310106  ... -0.239565 -0.194138 -0.553318   \n",
       "2939  0.585801 -0.136688  0.251222  ... -0.239565  0.574450  1.070736   \n",
       "2940 -0.216582 -0.138974 -0.310106  ...  4.174236 -0.194138 -0.807218   \n",
       "2941 -0.216582 -0.425902 -0.310106  ... -0.239565 -0.194138 -0.214785   \n",
       "\n",
       "           X15       X16       X17       X18       X19       X20       X21  \n",
       "0     0.979306 -0.146538 -0.141895 -0.556419 -0.288801 -0.404947 -0.101543  \n",
       "1    -0.558939  1.506001 -0.141895 -0.556419  0.526311  1.254308 -0.101543  \n",
       "2    -0.956568  1.518108 -0.141895  1.292904  0.719790  2.445281 -0.101543  \n",
       "3     0.106243 -0.960319 -0.141895  0.249005 -0.144716 -0.404947 -0.101543  \n",
       "4    -0.187675 -0.840624 -0.141895  1.249215 -0.144716 -0.404947 -0.101543  \n",
       "...        ...       ...       ...       ...       ...       ...       ...  \n",
       "2937 -0.452577 -0.344108 -0.141895 -0.195510  0.670980  2.654748 -0.101543  \n",
       "2938 -0.699354 -0.743298 -0.141895 -0.556419 -0.288801 -0.404947 -0.101543  \n",
       "2939  0.593058  0.503062 -0.141895 -0.556419  0.292931 -0.006986 -0.101543  \n",
       "2940  1.493734  0.387405 -0.141895  1.385371 -0.144716 -0.404947 -0.101543  \n",
       "2941 -0.056319 -1.120199 -0.141895 -0.556419 -0.144716 -0.404947 -0.101543  \n",
       "\n",
       "[2942 rows x 21 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "3df4e053",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 0, ..., 1, 0, 0], dtype=int64)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "d0bc8aaf",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 统计评估指标\n",
    "evaluation_ls = []"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d255043f",
   "metadata": {},
   "source": [
    "#### 2.1逻辑回归"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "63a462c6",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import roc_curve, auc\n",
    "test_label_list = test_y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "47680752",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "ba528b8e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 参数确定，计算acc\n",
    "model = LogisticRegression(random_state=0,penalty='l1',solver=\"liblinear\",C=0.1,max_iter=1000)\n",
    "\n",
    "model.fit(train_X,train_y) \n",
    "score_r = model.score(test_X,test_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "b30fa5ef",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Label</th>\n",
       "      <th>Precision</th>\n",
       "      <th>Recall</th>\n",
       "      <th>F1</th>\n",
       "      <th>Support</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0.935428</td>\n",
       "      <td>0.848101</td>\n",
       "      <td>0.889627</td>\n",
       "      <td>632</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0.542857</td>\n",
       "      <td>0.754967</td>\n",
       "      <td>0.631579</td>\n",
       "      <td>151</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>总体</td>\n",
       "      <td>0.859721</td>\n",
       "      <td>0.830140</td>\n",
       "      <td>0.839863</td>\n",
       "      <td>783</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Label  Precision    Recall        F1  Support\n",
       "0     0   0.935428  0.848101  0.889627      632\n",
       "1     1   0.542857  0.754967  0.631579      151\n",
       "2    总体   0.859721  0.830140  0.839863      783"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_label_list = test_y\n",
    "predict_label_list = model.predict(test_X)\n",
    "eval_model(test_label_list, predict_label_list, labelEncoder.classes_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "4d8101f3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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bcvfu3UqnXQPAnTt3yj3HRfOotmJyQ0RkhNzcXKhUKqPq/vjjjwgLC9Me//zzz4iKitIuivfkNgTu7u5616hbt65R067r1aunVxYWFob79++zx4ZqLSY3RERGEEIY1ZMCAEVFRTrHBQUFOm2N6VH59NNP8emnn5oeKICTJ09WqR2RrWByQ0RkQFxcHHJycuDp6Yno6GjIZDKjelIAQC6X6xy7uLho23IWEpHlcbYUEdmkJwfvtmnTBtu3b9ep079/f5w6dcpgey5kR1S9cLYUEdV6Tw7eDQoK0qtz69atSh81cSE7opqHyQ0RVXuGplAnJyfrJB5xcXGIi4vTHj85eNfHx0fvuj4+PhU+auIjJKKaickNEVV7hqZQP/lEPScnx2AvTEWDd598TEVEtoHJDRGVq7xF5wx588039Wb3KJVK5OXlVdp2+fLleOWVV7THCQkJGDBggPbY0BRqmUymcw1PT0+9Xhj2vBDVTkxuiKhc//vf/9CyZUucO3cO586dq7Du3bt39cquX79uVGJUUFCgc1xUVGRyL0x0dDSio6MrfS8isn1MboioXAsWLMDly5cxYMCASqdB161bV68sICDAqJ4bFxcXnWO5XM5eGCKqMk4FJyIiomrPlO9vOyvFRERERGQVfCxFVINlZGSgbdu2RtXds2cPmjdvrj3euHEjZs6cWW79sjEvYWFhXM6fiGoUJjdENVhJSYnR+x0VFxfrHOfn5xvV9v79+1WKjYhIKkxuiCSg0WiwZ88eLF++XKe8e/fuOH/+fKXt1Wo1xo8fD3t7e6P3O3Jw0P3P3dXVtdK2HMRLRDURkxsiCajVajRs2BCXL19GSEiItvzGjRtG9aaUzUDy9/ev8r5HQ4cOxdChQ6vUloioOmNyQ2RlGo0GKSkpSElJwYABA3D69GntOV9fX2RnZ1d6DXd3d0uGSERUozG5IbIytVqt/bmoqEjn3N69e60dDhGRzeFUcCIrKuu1KcPxLERE5sfkhsiKHu+1USqViIyMlDAaIiLbxOSGyErYa0NEZB1MboishL02RETWweSGyEqGDBmCgQMHAmCvDRGRJXHjTCIiIqr2uHEmERER1VpMbogsQKPRQKVSITAwUOdFRESWx0X8iCxArVbrzIwiIiLrYc8NkZk83ltTtvmlnZ0dFAqF9kVERJbHnhsiMzHUWxMaGork5GSJIiIiqp3Yc0NkAkNjafbv3w8AWLZsGZYvXw4AUCgUUCqVnPJNRCQBTgUnMoFKpdLrndm5cyciIiIkioiIqHYw5fubj6WITJCbmwvg0Vgaf39/AICTk5OUIRER0ROY3BBVgb+/P9LS0qQOg4iIDOCYGyIiIrIpTG6ITJCeni51CEREVAkmN0RV4OHhIXUIRERUDo65oVpr2LBhOHDgQKX1xo0bh5iYGACPpnh7eHhwijcRUTXG5IZqraysLKMeM2VnZ2t/5iBiIqLqj8kN2YSVK1figw8+KPd8eno6XF1d0b59e+zduxcA4O3tbdSWCF5eXmaLk4iILI/JDdmEvLy8Snth8vPzkZGRoT3esGGDpcMiIiIJSD6geOnSpWjUqBGcnZ0RFhaGQ4cOVVh/w4YNeO655+Dq6gp/f3+MHj0at2/ftlK0VF25u7vrbFBp6MXtEIiIagdJt1/YvHkzhg8fjqVLl6Jz585YsWIFVq1ahXPnzqFhw4Z69Q8fPoyuXbvi888/R79+/ZCeno6oqCg0a9YMW7duNeo9uf2C7cjIyEBJSQns7e21qwUTEZFtMuX7W9Kem7i4OIwZMwZjx46FSqXCwoULERQUhGXLlhms/9///hchISGYMmUKGjVqhL/97W+YMGECTp48aeXIqTpo27YtgoKC0LZtW6lDISKiakSy5KaoqAgJCQno1auXTnmvXr1w9OhRg206deqEtLQ07NixA0II3LhxA99//z369u1b7vsUFhYiJydH50U1n0aj4YJ6RERkkGTJTVZWFkpKSuDr66tT7uvri8zMTINtOnXqhA0bNmDw4MGQy+Xw8/NDnTp1sHjx4nLfZ968efDy8tK+goKCzPo5SBpqtVr7MxfUIyKix0k+oFgmk+kcCyH0ysqcO3cOU6ZMgVqtRkJCAnbu3InU1FRERUWVe/1Zs2YhOztb+7p27ZpZ4yfr+PPPPxEYGKh9nT9/XnuOg4SJiOhxkk0F9/b2hr29vV4vzc2bN/V6c8rMmzcPnTt3xowZMwAArVq1gpubG7p06YIPP/zQ4KBSJycnODk5mf8DkFUVFxcbfAylVCoRGRkpQURERFRdSZbcyOVyhIWFIT4+HgMHDtSWx8fHY8CAAQbb5Ofnw8FBN2R7e3sAj3p8yHY5ODjoLbjHbRCIiMgQSRfxi46OxvDhwxEeHo6OHTti5cqVuHr1qvYx06xZs5Ceno5169YBAPr164dx48Zh2bJliIiIQEZGBqZNm4Z27dohICBAyo9CFta8eXNufUBEREaRNLkZPHgwbt++jQ8++AAZGRlo2bIlduzYgeDgYACP1jG5evWqtv6oUaOQm5uLL7/8En//+99Rp04ddO/eHf/+97+l+ghkIRqNBmq1GikpKZg9ezZatGiBoUOHSh0WERHVAJIu4icFLuJXM6hUKqSkpGiPlUolkpOTJYyIiIikZMr3N/eWomopNzcXAGBnZ4fQ0FCOrSEiIqMxuaFqzd/fnz02RERkEsnXuSEiIiIyJyY3VO2Eh4cjIyND6jCIiKiGYnJD1U5mZiZKS0sBcGsFIiIyHZMbkoRGo4FKpYJMJkPbtm0xYcIE7Tk/Pz8oFAoolUoOJCYiIpNxKjhJglO9iYjIFJwKTtXOjBkzsGnTJu1x2ZgaTvUmIiJzY3JDVnH37l2DG1+Ghoayx4aIiMyKyQ2ZTdmWCWUL8Lm7u2sfPdWtW5cbXxIRkVVwzA2ZzZPjaDw8PJCTkyNhREREZCs45oYk8fiWCf7+/nB3d5c4IiIiqo2Y3JBZaDQa7Zgaf39/pKWlSRwRERHVVlznhsxCrVZrf+bCe0REJCUmN2QWj4+14SBhIiKSEpMbMov58+cDeLQYX2RkpMTREBFRbcbkhsxCpVJxuwQiIqoWOBWciIiIqj1Tvr/Zc0NPpWwDzISEBKlDISIiAsDkhp6SWq1GSkoKzp8/L3UoREREAJjc0FMqW7hv6NChEkdCRET0CJMbMosn940iIiKSCpMbIiIisilMbsgkZQOIAwMDERgYiIyMDKlDIiIi0sG9pcgkZQOIn8QtF4iIqLpgckMmOX78OFJTUzFw4EAUFhYCeJTYcPE+IiKqLpjckEk8PDzQqlUrXLx4UepQiIiIDOKYG6qURqOBTCbDqFGjEBcXJ3U4REREFeL2C1QplUqlHWejVCqRnJwscURERFTbcPsFMquyhfoAcGwNERFVe0xuyGgKhQKRkZFSh0FERFQhJjdERERkU5jcEBERkU2pUnJTXFyM3377DStWrNCOx7h+/Try8vLMGhwRERGRqUxe5+bKlSt4+eWXcfXqVRQWFqJnz57w8PDA/Pnz8eDBAyxfvtwScRIREREZxeSem6lTpyI8PBx3796Fi4uLtnzgwIHYs2ePWYMjIiIiMpXJPTeHDx/GkSNHIJfLdcqDg4ORnp5utsCo+mjTpg2CgoLg4+MjdShERESVMjm5KS0tRUlJiV55WloaN0+0Udu3b5c6BCIiIqOZ/FiqZ8+eWLhwofZYJpMhLy8PMTEx6NOnjzljIyIiIjKZydsvXL9+Hd26dYO9vT0uXLiA8PBwXLhwAd7e3jh48CAaNGhgqVjNgtsvEBER1TymfH+b/FgqICAASUlJ+Pbbb5GQkIDS0lKMGTMGw4YN0xlgTLahf//+uHXrFnx8fPh4ioiIagSTe24OHjyITp06wcFBNy8qLi7G0aNH8cILL5g1QHNjz41pAgMDkZ6eDoVCgbS0NKnDISKiWsqiG2d269YNd+7c0SvPzs5Gt27dTL0cERERkVmZnNwIISCTyfTKb9++DTc3N7MERURERFRVRo+5ee211wA8mh01atQoODk5ac+VlJTgzJkz6NSpk/kjJCIiIjKB0cmNl5cXgEc9Nx4eHjqDh+VyOTp06IBx48aZP0IiIiIiExid3KxZswYAEBISgvfee4+PoIiIiKhaMnkqeExMjCXioGooLi4OGRkZUodBRERkEpOTGwD4/vvv8d133+Hq1asoKirSOXfq1CmzBEbSy8nJQWlpKQBwaw0iIqoxTJ4ttWjRIowePRoNGjRAYmIi2rVrh/r16+PSpUvo3bu3JWIkiXh6ekKhUECpVGLu3LlSh0NERGQUkxfxUyqViImJwZtvvgkPDw+cPn0ajRs3hlqtxp07d/Dll19aKlaz4CJ+5dNoNFCr1cjNzUVycjJ7a4iIqNqw6CJ+V69e1U75dnFxQW5uLgBg+PDh2LRpUxXCpepCrVYjJSUF6enpMDHnJSIiqjZMTm78/Pxw+/ZtAEBwcDD++9//AgBSU1P5hViDaTQapKSkaI8NLdRIRERUE5ic3HTv3h0//fQTAGDMmDGYPn06evbsicGDB2PgwIFmD5CsQ61Wa39WKpV8JEVERDWWybOlVq5cqZ1BExUVhXr16uHw4cPo168foqKizB4gWd6TvTYcPExERDWZyQOKK1K2e3R1xgHF+lQqlTa5USqVSE5OljgiIiIiXRYdUGxIZmYm3n33XTRt2tTktkuXLkWjRo3g7OyMsLAwHDp0qML6hYWFmD17NoKDg+Hk5IQmTZrg66+/rmroBLDXhoiIbIrRyc29e/cwbNgw+Pj4ICAgAIsWLUJpaSnUajUaN26M//73vyYnGZs3b8a0adMwe/ZsJCYmokuXLujduzeuXr1abptBgwZhz549WL16Nf78809s2rQJSqXSpPclXRs3bgTwqNcmMjJS4miIiIiejtGPpSZNmoSffvoJgwcPxs6dO5GcnIyIiAg8ePAAMTEx6Nq1q8lv3r59e7Rp0wbLli3TlqlUKrz66quYN2+eXv2dO3diyJAhuHTpEurVq2fy+wF8LGVIQkIC3nrrLcydO5fJDRERVUsWeSz1yy+/YM2aNViwYAG2b98OIQRCQ0Oxd+/eKiU2RUVFSEhIQK9evXTKe/XqhaNHjxpss337doSHh2P+/PlQKBQIDQ3Fe++9h4KCgnLfp7CwEDk5OTov0hUWFobk5GQmNkREZBOMni11/fp1tGjRAgDQuHFjODs7Y+zYsVV+46ysLJSUlMDX11en3NfXF5mZmQbbXLp0CYcPH4azszO2bt2KrKwsTJo0CXfu3Cn3kdi8efMQGxtb5Tht2c8//4yCggK4uLjglVdekTocIiIiszA6uSktLYWjo6P22N7eHm5ubk8dwJOLxQkhyl1ArrS0FDKZDBs2bICXlxeARztXR0ZGYsmSJXBxcdFrM2vWLERHR2uPc3JyEBQU9NRx13QajQaDBg0CACgUCqSlpUkcERERkXkYndwIITBq1Cg4OTkBAB48eICoqCi9BGfLli1GXc/b2xv29vZ6vTQ3b97U680p4+/vD4VCoU1sgEdjdIQQSEtLQ7NmzfTaODk5aWOm//P4on1csI+IiGyJ0WNuRo4ciQYNGsDLywteXl546623EBAQoD0uexlLLpcjLCwM8fHxOuXx8fHavaue1LlzZ1y/fh15eXnasvPnz8POzg6BgYFGvzdBuycYwOnfRERkW8y6iJ+pNm/ejOHDh2P58uXo2LEjVq5cia+++gpnz55FcHAwZs2ahfT0dKxbtw4AkJeXB5VKhQ4dOiA2NhZZWVkYO3Ysunbtiq+++sqo9+RsqUcCAwO1iy7ykRQREVV3pnx/m7z9gjkNHjwYt2/fxgcffICMjAy0bNkSO3bsQHBwMAAgIyNDZ80bd3d3xMfH491330V4eDjq16+PQYMG4cMPP5TqIxAREVE1I2nPjRTYc/MIe26IiKgmsfr2C0RERETVBZMbIiIisilMbmopd3d3eHh4wN3dXepQiIiIzKpKyc369evRuXNnBAQE4MqVKwCAhQsX4scffzRrcGQ5KSkpyMnJ0dkRnIiIyBaYnNwsW7YM0dHR6NOnD+7du4eSkhIAQJ06dbBw4UJzx0dERERkEpOTm8WLF+Orr77C7NmzYW9vry0PDw/H77//btbgyHw0Gg1UKhUCAwMRGBiIGTNmSB0SERGRRZi8zk1qaipat26tV+7k5IT79++bJSgyP7VarfMI6u7duxJGQ0REZDkm99w0atQISUlJeuW//vqrdtdwqn7Ktluws7ODQqFA3bp1JY6IiIjIMkzuuZkxYwbeeecdPHjwAEIIHD9+HJs2bcK8efOwatUqS8RIZuTv789F+4iIyKaZnNyMHj0axcXFmDlzJvLz8zF06FAoFAp88cUXGDJkiCVipKeg0WigVquRkZEhdShERERW8VTbL2RlZaG0tBQNGjQwZ0wWVdu2X1CpVDpjbZRKJZKTkyWMiIiIyHQW3X4hNjYWFy9eBAB4e3vXqMSmNnrhhRcQHh4O4FFiM3fuXIkjIiIisiyTe25atWqFs2fPom3btnjrrbcwePBg+Pj4WCo+s6ttPTdERES2wKI9N2fOnMGZM2fQvXt3xMXFQaFQoE+fPti4cSPy8/OrHDQRERGROVRp+4VnnnkGH3/8MS5duoR9+/ahUaNGmDZtGvz8/MwdHz0FjUaD7t27Sx0GERGRVT31xplubm5wcXGBXC7Hw4cPzRETmYlarcb58+dx+fJlqUMhIiKymiolN6mpqfjoo4/QokULhIeH49SpU5gzZw4yMzPNHR89hdzcXKSnp2PAgAFSh0JERGQ1Jq9z07FjRxw/fhzPPvssRo8erV3nhqqv27dvSx0CERGR1Zic3HTr1g2rVq3CM888Y4l4iIiIiJ6KycnNxx9/bIk4iIiIiMzCqOQmOjoac+fOhZubG6KjoyusGxcXZ5bAiIiIiKrCqOQmMTFROxMqMTHRogERERERPQ2jkpt9+/YZ/JmIiIioujF5Kvjbb7+N3NxcvfL79+/j7bffNktQRERERFVlcnLzzTffoKCgQK+8oKAA69atM0tQRERERFVl9GypnJwcCCEghEBubi6cnZ2150pKSrBjxw7uEF7NzJ8/H/n5+XB1dZU6FCIiIqsxOrmpU6cOZDIZZDIZQkND9c7LZDLExsaaNTh6OkOHDpU6BCIiIqszOrnZt28fhBDo3r07fvjhB9SrV097Ti6XIzg4GAEBARYJkoyn0WigVquRm5uLPXv2oHnz5lKHREREZFVGJzddu3YF8GhfqYYNG0Imk1ksKKo6tVqNlJQUAEBxcbHE0RAREVmfUcnNmTNn0LJlS9jZ2SE7Oxu///57uXVbtWpltuDIdI/PZHNwMHkBaiIiohrPqG+/559/HpmZmWjQoAGef/55yGQyCCH06slkMpSUlJg9SDKdQqHgIykiIqqVjEpuUlNT4ePjo/2ZqieNRoP09HSpwyAiIpKUUclNcHCwwZ+pelGr1dqfPTw8JIyEiIhIOlVaxO+XX37RHs+cORN16tRBp06dcOXKFbMGR6Z5fLzN3LlzJYyEiIhIOiYnNx9//DFcXFwAAMeOHcOXX36J+fPnw9vbG9OnTzd7gGS8oqIiAI/G20RGRkocDRERkTRMTm6uXbuGpk2bAgC2bduGyMhIjB8/HvPmzcOhQ4fMHiAZb8uWLQD4SIqIiGo3k5Mbd3d33L59GwCwe/du9OjRAwDg7OxscM8psp4mTZpAqVTykRQREdVqJi+E0rNnT4wdOxatW7fG+fPn0bdvXwDA2bNnERISYu74yAT+/v5ITk6WOgwiIiJJmdxzs2TJEnTs2BG3bt3CDz/8gPr16wMAEhIS8Oabb5o9QKqcRqOBSqXCypUrpQ6FiIhIcjJhaDU+G5aTkwMvLy9kZ2fD09NT6nDMQqVSISUlBe+++y4WLVokdThERERmZ8r3d5WSm3v37mH16tVITk6GTCaDSqXCmDFj4OXlVeWgrcXWkhuNRoNBgwZpj2tZrkpERLWEKd/fJj+WOnnyJJo0aYLPP/8cd+7cQVZWFj7//HM0adIEp06dqnLQVDWPL9ynVColjISIiKh6MHlA8fTp09G/f3989dVX2o0Zi4uLMXbsWEybNg0HDx40e5BUPi7cR0REpMvkx1IuLi5ITEzU6yU4d+4cwsPDkZ+fb9YAzc3WHksFBgYiPT0dCoUCaWlpUodDRERkERZ9LOXp6YmrV6/qlV+7do2LxxEREZHkTE5uBg8ejDFjxmDz5s24du0a0tLS8O2332Ls2LGcCk5ERESSM3nMzYIFCyCTyTBixAgUFxcDABwdHTFx4kR88sknZg+QiIiIyBRVXucmPz8fFy9ehBACTZs2haurq7ljswhbG3PTvXt33LhxA76+vti7d6/U4RAREVmEKd/fRvfc5OfnY8aMGdi2bRsePnyIHj16YNGiRfD29n7qgKnqmNAQERHpMnrMTUxMDNauXYu+fftiyJAhiI+Px8SJEy0ZGxEREZHJjO652bJlC1avXo0hQ4YAAN566y107twZJSUlsLe3t1iARERERKYwOrm5du0aunTpoj1u164dHBwccP36dQQFBVkkOKrYsGHDkJWVBW9vb2zYsEHqcIiIiKoFo5ObkpISyOVy3cYODtoZU2RdGo0GGzduBAAoFAqJoyEiIqo+jE5uhBAYNWoUnJyctGUPHjxAVFQU3NzctGVbtmwxb4Rk0ON7SnHxRCIiov9jdHIzcuRIvbK33nrLrMGQ8binFBERkWFGJzdr1qyxZBxURQqFApGRkVKHQUREVG2YvP2CuS1duhSNGjWCs7MzwsLCcOjQIaPaHTlyBA4ODnj++ectGyARERHVKJImN5s3b8a0adMwe/ZsJCYmokuXLujdu7fBjTkfl52djREjRuCll16yUqRERERUU0ia3MTFxWHMmDEYO3YsVCoVFi5ciKCgICxbtqzCdhMmTMDQoUPRsWNHK0VKRERENYVkyU1RURESEhLQq1cvnfJevXrh6NGj5bZbs2YNLl68iJiYGEuHSERERDWQybuCm0tWVhZKSkrg6+urU+7r64vMzEyDbS5cuID3338fhw4dgoODcaEXFhaisLBQe5yTk1P1oKuRcePGITs7G15eXlKHQkREVK1UKblZv349li9fjtTUVBw7dgzBwcFYuHAhGjVqhAEDBph0LZlMpnMshNArAx4tIjh06FDExsYiNDTU6OvPmzcPsbGxJsVUE7DnioiIyDCTH0stW7YM0dHR6NOnD+7du4eSkhIAQJ06dbBw4UKjr+Pt7Q17e3u9XpqbN2/q9eYAj9Z1OXnyJCZPngwHBwc4ODjggw8+wOnTp+Hg4FDu7tizZs1Cdna29nXt2jXjPywRERHVOCYnN4sXL8ZXX32F2bNn62yYGR4ejt9//93o68jlcoSFhSE+Pl6nPD4+Hp06ddKr7+npid9//x1JSUnaV1RUFJo3b46kpCS0b9/e4Ps4OTnB09NT51XTaTQaqFQqqcMgIiKqlkx+LJWamorWrVvrlTs5OeH+/fsmXSs6OhrDhw9HeHg4OnbsiJUrV+Lq1auIiooC8KjXJT09HevWrYOdnR1atmyp075BgwZwdnbWK7d1arUaKSkpuHz5MkJCQqQOh4iIqFoxOblp1KgRkpKSEBwcrFP+66+/okWLFiZda/Dgwbh9+zY++OADZGRkoGXLltixY4f22hkZGZWueVMblW29MGDAAJw+fVriaIiIiKoXmRBCmNJgzZo1+Ne//oXPPvsMY8aMwapVq3Dx4kXMmzcPq1atwpAhQywVq1nk5OTAy8sL2dnZNfYRVWBgINLT06FQKJCWliZ1OERERBZnyve3yT03o0ePRnFxMWbOnIn8/HwMHToUCoUCX3zxRbVPbIiIiMj2VWkq+Lhx4zBu3DhkZWWhtLQUDRo0MHdcRERERFXyVIv4eXt7mysOIiIiIrOo0oBiQ4vslbl06dJTBURERET0NExObqZNm6Zz/PDhQyQmJmLnzp2YMWOGueIiIiIiqhKTk5upU6caLF+yZAlOnjz51AFR5dLT06UOgYiIqNoy267gvXv3xg8//GCuy1EFli9fDgDw8PCQOBIiIqLqx2zJzffff4969eqZ63JUgebNm0OpVGLu3LlSh0JERFTtmPxYqnXr1joDioUQyMzMxK1bt7B06VKzBkeGvfjii0hOTpY6DCIiomrJ5OTm1Vdf1Tm2s7ODj48PXnzxRSiVSnPFRURERFQlJiU3xcXFCAkJQUREBPz8/CwVE1Vg//79KCwshJOTE1588UWpwyEiIqp2TEpuHBwcMHHiRD4SsSCNRgO1Wq3dHPNJZTOluK8UERGRYSY/lmrfvj0SExP1dgUn81Cr1UhJSam0HmdKERERGWZycjNp0iT8/e9/R1paGsLCwuDm5qZzvlWrVmYLrjZKTk7G5cuXMWDAANy+fdtgHQ8PD86UIiIiKodMCCGMqfj2229j4cKFqFOnjv5FZDIIISCTyVBSUmLuGM3KlC3TiYiIqHow5fvb6OTG3t4eGRkZKCgoqLBedX9cxeSGiIio5jHl+9vox1JlOVB1T16IiIiodjNpzE1Fu4GTecTGxiI7OxteXl6IiYmROhwiIqIax+jHUnZ2dvDy8qo0wblz545ZArOU6vxYSqPRYNCgQQA41ZuIiOhxFnksBTzqVfDy8nqq4Kh8arVa+zOnehMREVWNScnNkCFD0KBBA0vFUus9vnAfp3oTERFVjdG7gnO8jfUoFApERkZKHQYREVGNZHRyY+TQHKoijUaj3VqBiIiIqs7ox1KlpaWWjKPW43gbIiIi8zC654Ysi+NtiIiIzMPkvaXIMrp27YqsrCx4e3tzvA0REdFTYHJTTWzYsEHqEIiIiGwCH0sRERGRTWFyQ0RERDaFyQ0RERHZFCY31UT37t3xzDPPoHv37lKHQkREVKNxQHE1cf78eaSnpyM7O1vqUIiIiGo09twQERGRTWFyUw1w6wUiIiLzYXJTDXDrBSIiIvNhclMNcOsFIiIi82FyU40oFApuvUBERPSUmNwQERGRTWFyQ0RERDaF69xUA2q1Gnl5eXB3d5c6FCIiohqPyU01MH78eKlDICIishl8LEVEREQ2hcmNRDQaDVQqFRo0aIDDhw8jIyND6pCIiIhsgkwIIaQOwppycnLg5eWF7OxseHp6ShaHSqVCSkqK9lipVCI5OVmyeIiIiKozU76/OeZGImUL99nZ2SE0NJSL9xEREZkJkxuJ+fv7s8eGiIjIjDjmhoiIiGwKkxsiIiKyKUxuiIiIyKYwuSEiIiKbwuSGiIiIbApnS0lkz549KC4uhoMDfwVERETmxG9WiTRv3lzqEIiIiGwSH0sRERGRTWHPjQQ2btyI/Px8uLq6YujQoVKHQ0REZFO4t5QEAgMDkZ6eDoVCgbS0NEliICIiqklM+f6W/LHU0qVL0ahRIzg7OyMsLAyHDh0qt+6WLVvQs2dP+Pj4wNPTEx07dsSuXbusGC0RERFVd5ImN5s3b8a0adMwe/ZsJCYmokuXLujduzeuXr1qsP7BgwfRs2dP7NixAwkJCejWrRv69euHxMREK0dORERE1ZWkj6Xat2+PNm3aYNmyZdoylUqFV199FfPmzTPqGs888wwGDx4MtVptVH0+liIiIqp5asRjqaKiIiQkJKBXr1465b169cLRo0eNukZpaSlyc3NRr149S4RIRERENZBks6WysrJQUlICX19fnXJfX19kZmYadY3PPvsM9+/fx6BBg8qtU1hYiMLCQu1xTk5O1QImIiKiGkHyAcUymUznWAihV2bIpk2bMGfOHGzevBkNGjQot968efPg5eWlfQUFBT11zERERFR9SZbceHt7w97eXq+X5ubNm3q9OU/avHkzxowZg++++w49evSosO6sWbOQnZ2tfV27du2pY38aGo0G6enpksZARERkyyRLbuRyOcLCwhAfH69THh8fj06dOpXbbtOmTRg1ahQ2btyIvn37Vvo+Tk5O8PT01HlJ6fHB0x4eHhJGQkREZJskXaE4Ojoaw4cPR3h4ODp27IiVK1fi6tWriIqKAvCo1yU9PR3r1q0D8CixGTFiBL744gt06NBB2+vj4uICLy8vyT6HKfbu3YvLly9j+PDhmDp1qtThEBER2RxJk5vBgwfj9u3b+OCDD5CRkYGWLVtix44dCA4OBgBkZGTorHmzYsUKFBcX45133sE777yjLR85ciTWrl1r7fCrLCQkpMLFComIiKjquP0CERERVXs1Yp0bIiIiIkvgruBWNmHCBNy5cwf16tXDihUrpA6HiIjI5jC5sbJffvlFu/UCERERmR8fSxEREZFNYXJDRERENoXJDREREdkUJjdERERkU5jcEBERkU1hckNEREQ2hckNERER2RQmN0RERGRTuIiflb355pu4e/cu6tatK3UoRERENonJjZV9+umnUodARERk0/hYioiIiGwKkxsiIiKyKUxurEipVMLT0xNKpVLqUIiIiGwWkxsrysvLQ25uLvLy8qQOhYiIyGYxuSEiIiKbwuSGiIiIbAqTGyIiIrIpTG6sQKPRQKVSISMjQ+pQiIiIbB4X8bMCtVqNlJQU7bGHh4eE0RAREdk29txYQW5urvZnpVKJuXPnShgNERGRbWPPjRUsX74cBQUFcHFxwSuvvCJ1OERERDaNyY0VMKEhIiKyHj6WIiIiIpvCnhsLS0hIQFFREeRyOcLCwqQOh4iIyOYxubGwAQMGID09HQqFAmlpaVKHQ0REZPP4WIqIiIhsCpMbIiIisilMboiIiMimMLmxII1Gg/T0dKnDICIiqlWY3FiQWq3W/swtF4iIiKyDyY0FPb7tArdcICIisg4mN1agUCgQGRkpdRhERES1ApMbIiIisilcxM+CkpOTIYSATCaTOhQiIqJag8mNBXEQMRERkfXxsRQRERHZFCY3REREZFOY3FiIRqOBTCbDqFGjEBcXJ3U4REREtYZMCCGkDsKacnJy4OXlhezsbHh6elrsfVQqFVJSUgAASqUSycnJFnsvIiIiW2fK9zd7bsxMo9FApVLh/Pnz2jIu4EdERGQ97Lkxs8d7bAD22hAREZmDKd/fnApuZmVbLtjZ2SE0NJS9NkRERFbG5MZC/P392WNDRFYnhEBxcTFKSkqkDoXIZI6OjrC3t3/q6zC5ISKyEUVFRcjIyEB+fr7UoRBViUwmQ2BgINzd3Z/qOkxuzKxNmzYICgqCj4+P1KEQUS1SWlqK1NRU2NvbIyAgAHK5nFu/UI0ihMCtW7eQlpaGZs2aPVUPDpMbM9u+fbvUIRBRLVRUVITS0lIEBQXB1dVV6nCIqsTHxweXL1/Gw4cPnyq54VRwIiIbYmfH/61TzWWu3kb+V0BEREQ2hY+lzKh///64desWfHx8+HiKiIhIIuy5MaNTp07hv//9L06dOiV1KEREVEV79+6FUqlEaWmp1KHYnMjISKvst8jkhoiIJDVq1CjIZDLIZDI4ODigYcOGmDhxIu7evatX9+jRo+jTpw/q1q0LZ2dnPPvss/jss88Mruuzb98+9OnTB/Xr14erqytatGiBv//970hPT68wnpkzZ2L27Nk2PX5p6dKlaNSoEZydnREWFoZDhw5V2mbDhg147rnn4OrqCn9/f4wePRq3b9/Wnl+7dq329/j468GDB9o6arUaH330EXJycizyucrY7m+OiIhqjJdffhkZGRm4fPkyVq1ahZ9++gmTJk3SqbN161Z07doVgYGB2LdvH1JSUjB16lR89NFHGDJkCB7fTWjFihXo0aMH/Pz88MMPP+DcuXNYvnw5srOz8dlnn5Ubx9GjR3HhwgW88cYbT/V5ioqKnqq9JW3evBnTpk3D7NmzkZiYiC5duqB37964evVquW0OHz6MESNGYMyYMTh79iw0Gg1OnDiBsWPH6tTz9PRERkaGzsvZ2Vl7vlWrVggJCcGGDRss9vkAAKKWyc7OFgBEdna22a+tUCgEAKFQKMx+bSKiihQUFIhz586JgoICqUMx2ciRI8WAAQN0yqKjo0W9evW0x3l5eaJ+/fritdde02u/fft2AUB8++23Qgghrl27JuRyuZg2bZrB97t79265sbz77rsiMjJSp+yvv/4S/fv3Fw0aNBBubm4iPDxcxMfH69QJDg4Wc+fOFSNHjhSenp5ixIgRQgghjhw5Irp06SKcnZ1FYGCgePfdd0VeXp623fr160VYWJhwd3cXvr6+4s033xQ3btwoNz5zaNeunYiKitIpUyqV4v333y+3zaeffioaN26sU7Zo0SIRGBioPV6zZo3w8vKq9P3nzJkjunTpYvBcRX/Hpnx/s+eGiMjGxcXFITAwsNJX//799dr279/fqLbmHEdx6dIl7Ny5E46Ojtqy3bt34/bt23jvvff06vfr1w+hoaHYtGkTAECj0aCoqAgzZ840eP06deqU+94HDx5EeHi4TlleXh769OmD3377DYmJiYiIiEC/fv30ejo+/fRTtGzZEgkJCfjXv/6F33//HREREXjttddw5swZbN68GYcPH8bkyZO1bYqKijB37lycPn0a27ZtQ2pqKkaNGlXh/YmKioK7u3uFr/J6YYqKipCQkIBevXrplPfq1QtHjx4t9z07deqEtLQ07NixA0II3LhxA99//z369u2rd6+Cg4MRGBiIV155BYmJiXrXateuHY4fP47CwsIKP+dTqTT9sbAlS5aIkJAQ4eTkJNq0aSMOHjxYYf39+/eLNm3aCCcnJ9GoUSOxbNkyk96PPTdEZIsq+hdvTEyMAFDpq0OHDnptO3ToYFTbmJiYKsc+cuRIYW9vL9zc3ISzs7P2mnFxcdo6n3zyiQBQbq9L//79hUqlEkIIMXHiROHp6VmlWLy8vMS6desqrdeiRQuxePFi7XFwcLB49dVXdeoMHz5cjB8/Xqfs0KFDws7OrtwetuPHjwsAIjc3t9z3vnHjhrhw4UKFr4cPHxpsm56eLgCII0eO6JR/9NFHIjQ0tMLPrNFohLu7u3BwcBAARP/+/UVRUZH2/LFjx8T69etFUlKSOHjwoHj99deFi4uLOH/+vM51Tp8+LQCIy5cv672HuXpuJJ0KXvbcb+nSpejcuTNWrFiB3r1749y5c2jYsKFe/dTUVPTp0wfjxo3Df/7zHxw5cgSTJk2Cj48PXn/9dQk+ARFR9efp6QmFQlFpPUPbxvj4+BjV1tPTs0qxlenWrRuWLVuG/Px8rFq1CufPn8e7776rV088Nq7myfKyBeAe/9lUBQUFOmNEAOD+/fuIjY3Fzz//jOvXr6O4uBgFBQV6vSNP9vgkJCTgr7/+0hlfIoTQbpWhUqmQmJiIOXPmICkpCXfu3NHO0Lp69SpatGhhMMYGDRqgQYMGVfp8ZZ68P5Xds3PnzmHKlClQq9WIiIhARkYGZsyYgaioKKxevRoA0KFDB3To0EHbpnPnzmjTpg0WL16MRYsWactdXFwAwKJ7oEma3MTFxWHMmDHaAUkLFy7Erl27sGzZMsybN0+v/vLly9GwYUMsXLgQAKBSqXDy5EksWLCAyQ0RUTmio6MRHR1dpbbWWrPLzc0NTZs2BQAsWrQI3bp1Q2xsLObOnQsACA0NBQAkJyejU6dOeu1TUlK0yUBoaCiys7ORkZEBf39/k+Lw9vbWm6U1Y8YM7Nq1CwsWLEDTpk3h4uKCyMhIvUHDbm5uOselpaWYMGECpkyZovc+DRs2xP3799GrVy/06tUL//nPf+Dj44OrV68iIiKiwgHJUVFR+M9//lPh5yivk8Db2xv29vbIzMzUKb958yZ8fX3Lvd68efPQuXNnzJgxA8CjgcFubm7o0qULPvzwQ4P32c7ODm3btsWFCxd0yu/cuQPAcDJtLpKNuanKc79jx47p1Y+IiMDJkyfx8OFDg20KCwuRk5Oj8yIiouotJiYGCxYswPXr1wE8+m6oV6+ewZlO27dvx4ULF/Dmm28CeLSWilwux/z58w1e+969e+W+b+vWrXHu3DmdskOHDmHUqFEYOHAgnn32Wfj5+eHy5cuVfoY2bdrg7NmzaNq0qd5LLpcjJSUFWVlZ+OSTT9ClSxcolUrcvHmz0ut+8MEHSEpKqvAVEBBgsK1cLkdYWBji4+N1yuPj4w0mjWXy8/P1psaX7f1UUW9aUlKSXuLzxx9/IDAwEN7e3pV+1qqSrOcmKysLJSUlepmir6+vXkZZJjMz02D94uJiZGVlGcwc582bh9jYWPMFXoHo6Gjk5OQ8dfcsEVFt9+KLL+KZZ57Bxx9/jC+//BJubm5YsWIFhgwZgvHjx2Py5Mnw9PTEnj17MGPGDERGRmLQoEEAgKCgIHz++eeYPHkycnJyMGLECISEhCAtLQ3r1q2Du7t7udPBIyIi8M033+iUNW3aFFu2bEG/fv0gk8nwr3/9y6gF/v7xj3+gQ4cOeOeddzBu3Di4ubkhOTkZ8fHxWLx4MRo2bAi5XI7FixcjKioKf/zxh7anqiJP+1gqOjoaw4cPR3h4ODp27IiVK1fi6tWriIqK0taZNWsW0tPTsW7dOgCPBm2PGzcOy5Yt0z6WmjZtGtq1a6dNpGJjY9GhQwc0a9YMOTk5WLRoEZKSkrBkyRKd9z906JBeR4XZVToqx0LKBjUdPXpUp/zDDz8UzZs3N9imWbNm4uOPP9YpO3z4sAAgMjIyDLZ58OCByM7O1r6uXbtmsQHFRERSsbWp4EIIsWHDBiGXy8XVq1e1ZQcPHhQvv/yy8PLyEnK5XLRo0UIsWLBAFBcX67WPj48XERERom7dusLZ2VkolUrx3nvvievXr5cby507d4SLi4tISUnRlqWmpopu3boJFxcXERQUJL788kvRtWtXMXXqVG2d4OBg8fnnn+td7/jx46Jnz57C3d1duLm5iVatWomPPvpIe37jxo3aSTUdO3bUTmtPTEys+KY9pSVLlojg4GAhl8tFmzZtxIEDB3TOjxw5UnTt2lWnbNGiRaJFixbCxcVF+Pv7i2HDhom0tDTt+WnTpomGDRsKuVwufHx8RK9evfS+4wsKCoSnp6c4duyYwbjMNaBYJkQ5/UkWVlRUBFdXV2g0GgwcOFBbPnXqVCQlJeHAgQN6bV544QW0bt0aX3zxhbZs69atGDRoEPLz83WmDZYnJycHXl5eyM7OZg8LEdmMBw8eIDU1VbvqLFXdzJkzkZ2djRUrVkgdis1ZsmQJfvzxR+zevdvg+Yr+jk35/pZszE1Vnvt17NhRr/7u3bsRHh5uVGJDRERUmdmzZyM4ONjglg70dBwdHbF48WKLv4+ki/hFR0dj1apV+Prrr5GcnIzp06frPPebNWsWRowYoa0fFRWFK1euIDo6GsnJyfj666+xevVqg4s6ERERVYWXlxf++c9/agfMkvmMHz8ezZs3t/j7SDoVfPDgwbh9+zY++OADZGRkoGXLltixYweCg4MBABkZGTrrCDRq1Ag7duzA9OnTsWTJEgQEBGDRokWcBk5ERERako25kQrH3BCRLeKYG7IFNX7MDRERmV8t+/cq2Rhz/f0yuSEisgFlkyosuaQ9kaWVrcz8tOOdJB1zQ0RE5mFvb486depoV7h1dXWt8v5KRFIoLS3FrVu34OrqCgeHp0tPmNwQEdkIPz8/ADBqCX+i6sjOzg4NGzZ86sScyQ0RkY2QyWTw9/dHgwYNyt1vj6g6k8vlentYVQWTGyIiG2Nvb881WqhW44BiIiIisilMboiIiMimMLkhIiIim1LrxtyULRCUk5MjcSRERERkrLLvbWMW+qt1yU1ubi4AICgoSOJIiIiIyFS5ubnw8vKqsE6t21uqtLQU169fh4eHh9kXuMrJyUFQUBCuXbvGfassiPfZOnifrYP32Xp4r63DUvdZCIHc3FwEBARUOl281vXc2NnZITAw0KLv4enpyf9wrID32Tp4n62D99l6eK+twxL3ubIemzIcUExEREQ2hckNERER2RQmN2bk5OSEmJgYODk5SR2KTeN9tg7eZ+vgfbYe3mvrqA73udYNKCYiIiLbxp4bIiIisilMboiIiMimMLkhIiIim8LkhoiIiGwKkxsTLV26FI0aNYKzszPCwsJw6NChCusfOHAAYWFhcHZ2RuPGjbF8+XIrRVqzmXKft2zZgp49e8LHxweenp7o2LEjdu3aZcVoay5T/57LHDlyBA4ODnj++ectG6CNMPU+FxYWYvbs2QgODoaTkxOaNGmCr7/+2krR1lym3ucNGzbgueeeg6urK/z9/TF69Gjcvn3bStHWTAcPHkS/fv0QEBAAmUyGbdu2VdpGku9BQUb79ttvhaOjo/jqq6/EuXPnxNSpU4Wbm5u4cuWKwfqXLl0Srq6uYurUqeLcuXPiq6++Eo6OjuL777+3cuQ1i6n3eerUqeLf//63OH78uDh//ryYNWuWcHR0FKdOnbJy5DWLqfe5zL1790Tjxo1Fr169xHPPPWedYGuwqtzn/v37i/bt24v4+HiRmpoq/ve//4kjR45YMeqax9T7fOjQIWFnZye++OILcenSJXHo0CHxzDPPiFdffdXKkdcsO3bsELNnzxY//PCDACC2bt1aYX2pvgeZ3JigXbt2IioqSqdMqVSK999/32D9mTNnCqVSqVM2YcIE0aFDB4vFaAtMvc+GtGjRQsTGxpo7NJtS1fs8ePBg8f/+3/8TMTExTG6MYOp9/vXXX4WXl5e4ffu2NcKzGabe508//VQ0btxYp2zRokUiMDDQYjHaGmOSG6m+B/lYykhFRUVISEhAr169dMp79eqFo0ePGmxz7NgxvfoRERE4efIkHj58aLFYa7Kq3OcnlZaWIjc3F/Xq1bNEiDahqvd5zZo1uHjxImJiYiwdok2oyn3evn07wsPDMX/+fCgUCoSGhuK9995DQUGBNUKukapynzt16oS0tDTs2LEDQgjcuHED33//Pfr27WuNkGsNqb4Ha93GmVWVlZWFkpIS+Pr66pT7+voiMzPTYJvMzEyD9YuLi5GVlQV/f3+LxVtTVeU+P+mzzz7D/fv3MWjQIEuEaBOqcp8vXLiA999/H4cOHYKDA//XYYyq3OdLly7h8OHDcHZ2xtatW5GVlYVJkybhzp07HHdTjqrc506dOmHDhg0YPHgwHjx4gOLiYvTv3x+LFy+2Rsi1hlTfg+y5MZFMJtM5FkLolVVW31A56TL1PpfZtGkT5syZg82bN6NBgwaWCs9mGHufS0pKMHToUMTGxiI0NNRa4dkMU/6eS0tLIZPJsGHDBrRr1w59+vRBXFwc1q5dy96bSphyn8+dO4cpU6ZArVYjISEBO3fuRGpqKqKioqwRaq0ixfcg//llJG9vb9jb2+v9K+DmzZt6WWkZPz8/g/UdHBxQv359i8Vak1XlPpfZvHkzxowZA41Ggx49elgyzBrP1Pucm5uLkydPIjExEZMnTwbw6EtYCAEHBwfs3r0b3bt3t0rsNUlV/p79/f2hUCjg5eWlLVOpVBBCIC0tDc2aNbNozDVRVe7zvHnz0LlzZ8yYMQMA0KpVK7i5uaFLly748MMP2bNuJlJ9D7LnxkhyuRxhYWGIj4/XKY+Pj0enTp0MtunYsaNe/d27dyM8PByOjo4Wi7Umq8p9Bh712IwaNQobN27kM3MjmHqfPT098fvvvyMpKUn7ioqKQvPmzZGUlIT27dtbK/QapSp/z507d8b169eRl5enLTt//jzs7OwQGBho0Xhrqqrc5/z8fNjZ6X4F2tvbA/i/ngV6epJ9D1p0uLKNKZtquHr1anHu3Dkxbdo04ebmJi5fviyEEOL9998Xw4cP19YvmwI3ffp0ce7cObF69WpOBTeCqfd548aNwsHBQSxZskRkZGRoX/fu3ZPqI9QIpt7nJ3G2lHFMvc+5ubkiMDBQREZGirNnz4oDBw6IZs2aibFjx0r1EWoEU+/zmjVrhIODg1i6dKm4ePGiOHz4sAgPDxft2rWT6iPUCLm5uSIxMVEkJiYKACIuLk4kJiZqp9xXl+9BJjcmWrJkiQgODhZyuVy0adNGHDhwQHtu5MiRomvXrjr19+/fL1q3bi3kcrkICQkRy5Yts3LENZMp97lr164CgN5r5MiR1g+8hjH17/lxTG6MZ+p9Tk5OFj169BAuLi4iMDBQREdHi/z8fCtHXfOYep8XLVokWrRoIVxcXIS/v78YNmyYSEtLs3LUNcu+ffsq/P9tdfkelAnB/jciIiKyHRxzQ0RERDaFyQ0RERHZFCY3REREZFOY3BAREZFNYXJDRERENoXJDREREdkUJjdERERkU5jcEJGOtWvXok6dOlKHUWUhISFYuHBhhXXmzJmD559/3irxEJH1MbkhskGjRo2CTCbTe/31119Sh4a1a9fqxOTv749BgwYhNTXVLNc/ceIExo8frz2WyWTYtm2bTp333nsPe/bsMcv7lefJz+nr64t+/frh7NmzJl+nJiebRFJgckNko15++WVkZGTovBo1aiR1WAAebcSZkZGB69evY+PGjUhKSkL//v1RUlLy1Nf28fGBq6trhXXc3d0tuiNxmcc/5y+//IL79++jb9++KCoqsvh7E9VmTG6IbJSTkxP8/Px0Xvb29oiLi8Ozzz4LNzc3BAUFYdKkSTo7UD/p9OnT6NatGzw8PODp6YmwsDCcPHlSe/7o0aN44YUX4OLigqCgIEyZMgX379+vMDaZTAY/Pz/4+/ujW7duiImJwR9//KHtWVq2bBmaNGkCuVyO5s2bY/369Trt58yZg4YNG8LJyQkBAQGYMmWK9tzjj6VCQkIAAAMHDoRMJtMeP/5YateuXXB2dsa9e/d03mPKlCno2rWr2T5neHg4pk+fjitXruDPP//U1qno97F//36MHj0a2dnZ2h6gOXPmAACKioowc+ZMKBQKuLm5oX379ti/f3+F8RDVFkxuiGoZOzs7LFq0CH/88Qe++eYb7N27FzNnziy3/rBhwxAYGIgTJ04gISEB77//PhwdHQEAv//+OyIiIvDaa6/hzJkz2Lx5Mw4fPozJkyebFJOLiwsA4OHDh9i6dSumTp2Kv//97/jjjz8wYcIEjB49Gvv27QMAfP/99/j888+xYsUKXLhwAdu2bcOzzz5r8LonTpwAAKxZswYZGRna48f16NEDderUwQ8//KAtKykpwXfffYdhw4aZ7XPeu3cPGzduBADt/QMq/n106tQJCxcu1PYAZWRk4L333gMAjB49GkeOHMG3336LM2fO4I033sDLL7+MCxcuGB0Tkc2y+NacRGR1I0eOFPb29sLNzU37ioyMNFj3u+++E/Xr19cer1mzRnh5eWmPPTw8xNq1aw22HT58uBg/frxO2aFDh4SdnZ0oKCgw2ObJ61+7dk106NBBBAYGisLCQtGpUycxbtw4nTZvvPGG6NOnjxBCiM8++0yEhoaKoqIig9cPDg4Wn3/+ufYYgNi6datOnSd3NJ8yZYro3r279njXrl1CLpeLO3fuPNXnBCDc3NyEq6urdvfk/v37G6xfprLfhxBC/PXXX0Imk4n09HSd8pdeeknMmjWrwusT1QYO0qZWRGQp3bp1w7Jly7THbm5uAIB9+/bh448/xrlz55CTk4Pi4mI8ePAA9+/f19Z5XHR0NMaOHYv169ejR48eeOONN9CkSRMAQEJCAv766y9s2LBBW18IgdLSUqSmpkKlUhmMLTs7G+7u7hBCID8/H23atMGWLVsgl8uRnJysMyAYADp37owvvvgCAPDGG29g4cKFaNy4MV5++WX06dMH/fr1g4ND1f93NmzYMHTs2BHXr19HQEAANmzYgD59+qBu3bpP9Tk9PDxw6tQpFBcX48CBA/j000+xfPlynTqm/j4A4NSpUxBCIDQ0VKe8sLDQKmOJiKo7JjdENsrNzQ1NmzbVKbty5Qr69OmDqKgozJ07F/Xq1cPhw4cxZswYPHz40OB15syZg6FDh+KXX37Br7/+ipiYGHz77bcYOHAgSktLMWHCBJ0xL2UaNmxYbmxlX/p2dnbw9fXV+xKXyWQ6x0IIbVlQUBD+/PNPxMfH47fffsOkSZPw6aef4sCBAzqPe0zRrl07NGnSBN9++y0mTpyIrVu3Ys2aNdrzVf2cdnZ22t+BUqlEZmYmBg8ejIMHDwKo2u+jLB57e3skJCTA3t5e55y7u7tJn53IFjG5IapFTp48ieLiYnz22Wews3s05O67776rtF1oaChCQ0Mxffp0vPnmm1izZg0GDhyINm3a4OzZs3pJVGUe/9J/kkqlwuHDhzFixAht2dGjR3V6R1xcXNC/f3/0798f77zzDpRKJX7//Xe0adNG73qOjo5GzcIaOnQoNmzYgMDAQNjZ2aFv377ac1X9nE+aPn064uLisHXrVgwcONCo34dcLteLv3Xr1igpKcHNmzfRpUuXp4qJyBZxQDFRLdKkSRMUFxdj8eLFuHTpEtavX6/3mORxBQUFmDx5Mvbv348rV67gyJEjOHHihDbR+Mc//oFjx47hnXfeQVJSEi5cuIDt27fj3XffrXKMM2bMwNq1a7F8+XJcuHABcXFx2LJli3Yg7dq1a7F69Wr88ccf2s/g4uKC4OBgg9cLCQnBnj17kJmZibt375b7vsOGDcOpU6fw0UcfITIyEs7Oztpz5vqcnp6eGDt2LGJiYiCEMOr3ERISgry8POzZswdZWVnIz89HaGgohg0bhhEjRmDLli1ITU3FiRMn8O9//xs7duwwKSYimyTlgB8isoyRI0eKAQMGGDwXFxcn/P39hYuLi4iIiBDr1q0TAMTdu3eFELoDWAsLC8WQIUNEUFCQkMvlIiAgQEyePFlnEO3x48dFz549hbu7u3BzcxOtWrUSH330UbmxGRog+6SlS5eKxo0bC0dHRxEaGirWrVunPbd161bRvn174enpKdzc3ESHDh3Eb7/9pj3/5IDi7du3i6ZNmwoHBwcRHBwshNAfUFymbdu2AoDYu3ev3jlzfc4rV64IBwcHsXnzZiFE5b8PIYSIiooS9evXFwBETEyMEEKIoqIioVarRUhIiHB0dBR+fn5i4MCB4syZM+XGRFRbyIQQQtr0ioiIiMh8+FiKiIiIbAqTGyIiIrIpTG6IiIjIpjC5ISIiIpvC5IaIiIhsCpMbIiIisilMboiIiMimMLkhIiIim8LkhoiIiGwKkxsiIiKyKUxuiIiIyKYwuSEiIiKb8v8BpdBRHbyluhIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "0.8506475815240171"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predict_pro_list, AUC = get_roc_auc(model,test_X,test_label_list)\n",
    "AUC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8d71f21a",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "6291394b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 统计详情\n",
    "evaluation_now = list(eval_model(test_label_list, predict_label_list, labelEncoder.classes_).iloc[1,[1,2,3]].values)\n",
    "evaluation_now.append(score_r)\n",
    "evaluation_now.append(AUC)\n",
    "evaluation_ls.append(evaluation_now)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "06d950d1",
   "metadata": {},
   "source": [
    "#### 2.2KNeighborsClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "d9cb913a",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "e7335dc7",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = KNeighborsClassifier(n_neighbors=40)\n",
    "\n",
    "model.fit(train_X,train_y) \n",
    "score_r = model.score(test_X,test_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "cf83ea5f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Label</th>\n",
       "      <th>Precision</th>\n",
       "      <th>Recall</th>\n",
       "      <th>F1</th>\n",
       "      <th>Support</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0.925795</td>\n",
       "      <td>0.829114</td>\n",
       "      <td>0.874791</td>\n",
       "      <td>632</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0.502304</td>\n",
       "      <td>0.721854</td>\n",
       "      <td>0.592391</td>\n",
       "      <td>151</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>总体</td>\n",
       "      <td>0.844126</td>\n",
       "      <td>0.808429</td>\n",
       "      <td>0.820331</td>\n",
       "      <td>783</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Label  Precision    Recall        F1  Support\n",
       "0     0   0.925795  0.829114  0.874791      632\n",
       "1     1   0.502304  0.721854  0.592391      151\n",
       "2    总体   0.844126  0.808429  0.820331      783"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_label_list = test_y\n",
    "predict_label_list = model.predict(test_X)\n",
    "eval_model(test_label_list, predict_label_list, labelEncoder.classes_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "bed55270",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "0.8670309749350322"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predict_pro_list, AUC = get_roc_auc(model,test_X,test_label_list)\n",
    "AUC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "35fda1f3",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "ef51354f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 统计详情\n",
    "evaluation_now = list(eval_model(test_label_list, predict_label_list, labelEncoder.classes_).iloc[1,[1,2,3]].values)\n",
    "evaluation_now.append(score_r)\n",
    "evaluation_now.append(AUC)\n",
    "evaluation_ls.append(evaluation_now)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d420cf77",
   "metadata": {},
   "source": [
    "#### 2.3GaussianNB"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "f34ff2aa",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.naive_bayes import GaussianNB"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "5221cf36",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 训练\n",
    "model = GaussianNB()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "d6d2e04d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 测试\n",
    "model = model.fit(train_X,train_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "88dafe4f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Label</th>\n",
       "      <th>Precision</th>\n",
       "      <th>Recall</th>\n",
       "      <th>F1</th>\n",
       "      <th>Support</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0.841808</td>\n",
       "      <td>0.943038</td>\n",
       "      <td>0.889552</td>\n",
       "      <td>632</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0.520000</td>\n",
       "      <td>0.258278</td>\n",
       "      <td>0.345133</td>\n",
       "      <td>151</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>总体</td>\n",
       "      <td>0.779748</td>\n",
       "      <td>0.810983</td>\n",
       "      <td>0.784562</td>\n",
       "      <td>783</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Label  Precision    Recall        F1  Support\n",
       "0     0   0.841808  0.943038  0.889552      632\n",
       "1     1   0.520000  0.258278  0.345133      151\n",
       "2    总体   0.779748  0.810983  0.784562      783"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_label_list = test_y\n",
    "predict_label_list = model.predict(test_X)\n",
    "eval_model(test_label_list, predict_label_list, labelEncoder.classes_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "0f3cb595",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "0.7142782295246878"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predict_pro_list, AUC = get_roc_auc(model,test_X,test_label_list)\n",
    "AUC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ff8e2e26",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "8d1c4695",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 统计详情\n",
    "evaluation_now = list(eval_model(test_label_list, predict_label_list, labelEncoder.classes_).iloc[1,[1,2,3]].values)\n",
    "evaluation_now.append(score_r)\n",
    "evaluation_now.append(AUC)\n",
    "evaluation_ls.append(evaluation_now)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "494e50a1",
   "metadata": {},
   "source": [
    "#### 2.4RandomForestClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "0b0bc609",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "47fef70b",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = RandomForestClassifier(random_state=0,n_estimators=360)\n",
    "\n",
    "model.fit(train_X,train_y) \n",
    "score_r = model.score(test_X,test_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d12b6f6f",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "25892304",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Label</th>\n",
       "      <th>Precision</th>\n",
       "      <th>Recall</th>\n",
       "      <th>F1</th>\n",
       "      <th>Support</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0.924409</td>\n",
       "      <td>0.928797</td>\n",
       "      <td>0.926598</td>\n",
       "      <td>632</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0.695946</td>\n",
       "      <td>0.682119</td>\n",
       "      <td>0.688963</td>\n",
       "      <td>151</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>总体</td>\n",
       "      <td>0.880351</td>\n",
       "      <td>0.881226</td>\n",
       "      <td>0.880771</td>\n",
       "      <td>783</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Label  Precision    Recall        F1  Support\n",
       "0     0   0.924409  0.928797  0.926598      632\n",
       "1     1   0.695946  0.682119  0.688963      151\n",
       "2    总体   0.880351  0.881226  0.880771      783"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_label_list = test_y\n",
    "predict_label_list = model.predict(test_X)\n",
    "eval_model(test_label_list, predict_label_list, labelEncoder.classes_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "4a94b761",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "0.9211166065889849"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predict_pro_list, AUC = get_roc_auc(model,test_X,test_label_list)\n",
    "AUC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ad97f229",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "ba537fbc",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 统计详情\n",
    "evaluation_now = list(eval_model(test_label_list, predict_label_list, labelEncoder.classes_).iloc[2,[1,2,3]].values)\n",
    "evaluation_now.append(score_r)\n",
    "evaluation_now.append(AUC)\n",
    "evaluation_ls.append(evaluation_now)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "21e8f4c5",
   "metadata": {},
   "source": [
    "#### 2.5SVM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "d181b8f4",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.svm import SVC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "8e3f05a4",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = SVC(kernel='rbf', probability=True,random_state=0)\n",
    "\n",
    "model.fit(train_X,train_y) \n",
    "score_r = model.score(test_X,test_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "b93c1ab3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Label</th>\n",
       "      <th>Precision</th>\n",
       "      <th>Recall</th>\n",
       "      <th>F1</th>\n",
       "      <th>Support</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0.937931</td>\n",
       "      <td>0.860759</td>\n",
       "      <td>0.897690</td>\n",
       "      <td>632</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0.566502</td>\n",
       "      <td>0.761589</td>\n",
       "      <td>0.649718</td>\n",
       "      <td>151</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>总体</td>\n",
       "      <td>0.866302</td>\n",
       "      <td>0.841635</td>\n",
       "      <td>0.849869</td>\n",
       "      <td>783</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Label  Precision    Recall        F1  Support\n",
       "0     0   0.937931  0.860759  0.897690      632\n",
       "1     1   0.566502  0.761589  0.649718      151\n",
       "2    总体   0.866302  0.841635  0.849869      783"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_label_list = test_y\n",
    "predict_label_list = model.predict(test_X)\n",
    "eval_model(test_label_list, predict_label_list, labelEncoder.classes_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "ae66244f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "0.8640497946181576"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predict_pro_list, AUC = get_roc_auc(model,test_X,test_label_list)\n",
    "AUC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "19905e2a",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "78933632",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 统计详情\n",
    "evaluation_now = list(eval_model(test_label_list, predict_label_list, labelEncoder.classes_).iloc[1,[1,2,3]].values)\n",
    "evaluation_now.append(score_r)\n",
    "evaluation_now.append(AUC)\n",
    "evaluation_ls.append(evaluation_now)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9939411e",
   "metadata": {},
   "source": [
    "#### 2.6GBDT"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "109a14d5",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import GradientBoostingClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "e59a4858",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = GradientBoostingClassifier(n_estimators = 290, random_state=0)\n",
    "\n",
    "model.fit(train_X,train_y) \n",
    "score_r = model.score(test_X,test_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "43286ec9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Label</th>\n",
       "      <th>Precision</th>\n",
       "      <th>Recall</th>\n",
       "      <th>F1</th>\n",
       "      <th>Support</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0.956656</td>\n",
       "      <td>0.977848</td>\n",
       "      <td>0.967136</td>\n",
       "      <td>632</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0.897810</td>\n",
       "      <td>0.814570</td>\n",
       "      <td>0.854167</td>\n",
       "      <td>151</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>总体</td>\n",
       "      <td>0.945308</td>\n",
       "      <td>0.946360</td>\n",
       "      <td>0.945350</td>\n",
       "      <td>783</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Label  Precision    Recall        F1  Support\n",
       "0     0   0.956656  0.977848  0.967136      632\n",
       "1     1   0.897810  0.814570  0.854167      151\n",
       "2    总体   0.945308  0.946360  0.945350      783"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_label_list = test_y\n",
    "predict_label_list = model.predict(test_X)\n",
    "eval_model(test_label_list, predict_label_list, labelEncoder.classes_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "17febe22",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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tWrlyJTIzM2Ftba3YKTgxMbHE/cw4n4aIqHR8FJxIDyIjIzF8+HAArzYJLExuStpIjz0zRETax+SGSEtCQkIUf3590TpnZ2duB0BEVI74tBSRlry+GB57Y4iI9Ic9NyQ5b25Z8KY3e1HCwsKwatWqUs/bqlUrbNq0SamsTZs2uHnzJoD/myDs6urKp5uIiPSIyQ1JjjpbFrwuLS1Nra0EUlNTVcoePHig0pb7KBER6ReTG5KUorYsKI2tra1aWwnY29urlDk6OiItLU1xzAnCRET6x0fBSVK4ZQERkTTxUXCSDDc3N7Xq/frrr/j4448RERGBGzduICgoiD0oREQGij03VKGpu6P0wYMHERgYqONoiIhIX9hzQxVOaU8wFapevTr27NkDT09PAFBrLgwAmJmZvW2IREQkEUxuqFwcOXIENWvWxPXr15GQkFBsPV9fXyxatAju7u6YMGECF78jIiKNMbmhcrF8+XLcuXMHXbt2RX5+frH14uLikJiYyPkyRERUZkxuSCdeH4by9vbG0aNH4enpib/++kvfoRERkcQxuSGdeH0hPVtbWz1HQ0REhoR7S5HWvbmQnqOjo54jIiIiQ8LkhrTu9d2xC4ekiIiIyguTG9I67o5NRET6xOSGdIa7YxMRkT4wuSGtU2eHbSIiIl1hckNaN2bMGACvdsgmIiIqb3wUnN5acnIy8vPzYWxsDGdnZzRo0AA+Pj6cb0NERHrBjTOpzAoX6it87NvV1ZXbJRARkU5o8v3NYSkqs9cTG4DDUEREVDFwWIrKrPCRbyMjI3h7e3MYioiIKgQmN/TWnJ2dERcXp+8wiIiIADC5kZzXN6x8XadOnbBixQqlMn9/f6SkpJR6zrlz56J3796K4xs3buCTTz5BcnKydoImIiLSIiY3EvPmPJhCT548USlLSUlRa02arKwspeO8vDyldpxrQ0REFQmTG4mYNGkStmzZouhNMTIygrOzs+J9Ozs7lTZOTk5qndvS0lLp2MTEBK6urgBeJTaca0NERBUJkxuJePr0qVJvire3d6nzYKKjo8t0rbp16/KRbyIiqrCY3EhEtWrV2JtCREQELuKn73CIiIhIDVzEz0D4+PjAxsYGPj4++g6FiIiowuCwVAVX3KPdwKs9nQoKCpCZmamHyIiIiComJjcVXHGPdr+Oj2ITERH9HyY3Fdy8efMQFxeHyZMnKyYMv46Th4mIiJRxQjERERFVeJxQTERERAaLyQ0RERFJCpObCigyMhK+vr5wc3ODTCbDli1bEBMTo++wiIiIKgXOuamAfH19VZ6Q8vHxKXU7BSIiIqnS5PubT0tVQIVr2hRufsknooiIiNTH5KYCKVywr3Bnb2dnZ25QSUREpCEmNxXImwv2cXE+IiIizXFCcQXy+hYLPj4+HIoiIiIqA/bcVCBxcXEQQkAmk7HXhoiIqIyY3FQAkZGRePbsGYYOHarvUIiIiCo9DktVACEhIUhKSsKdO3f0HQoREVGlx+SmnL2+QF/h6+bNm7h8+TK6du2q7/CIiIgqPS7iV86KWqCvEBfqIyIiKlql2jhz2bJl8PLygrm5Ofz8/HDq1KkS62/atAnvvfceLC0t4ezsjIEDB+Lx48flFO3be32BPldXV8WLT0cRERFph16Tm23btmH8+PGYNm0aYmNj0bJlS3To0AEJCQlF1j99+jT69euHwYMH4+rVq4iMjMSFCxcwZMiQco787RUu0Ff4iouLQ/fu3fUdFhERUaWn1+QmPDwcgwcPxpAhQ+Dr64uFCxfC3d0dERERRdb/73//C09PT4wdOxZeXl748MMPMXz4cERHR5dz5GXXuHFjNGvWDI0bN9Z3KERERJKkt+QmNzcXMTExaN++vVJ5+/btcfbs2SLbNG/eHImJiThw4ACEEHjw4AG2b9+OTp06FXudnJwcpKenK730ae/evTh37hz27t2r1ziIiIikSm/JTWpqKvLz8+Ho6KhU7ujoiJSUlCLbNG/eHJs2bULPnj0hl8vh5OSEqlWrYvHixcVeZ86cObC1tVW83N3dtfo5iIiIqGLR+4RimUymdFy4Qm9Rrl27hrFjxyIkJAQxMTE4ePAg4uPjERQUVOz5p06dirS0NMXr3r17Wo2fiIiIKha9rVBsb28PY2NjlV6ahw8fqvTmFJozZw5atGiBSZMmAQAaNmwIKysrtGzZEt9//z2cnZ1V2piZmcHMzEz7H4CIiIgqJL313Mjlcvj5+SEqKkqpPCoqCs2bNy+yTVZWFoyMlEM2NjYG8KrHp6KLjIyETCaDs7MzunTpou9wiIiIJEmvw1LBwcFYvXo11q5di7i4OEyYMAEJCQmKYaapU6eiX79+ivqdO3fGzp07ERERgdu3b+PMmTMYO3YsmjRpAhcXF319DLWFhIQAAFJSUnDr1i09R0NERCRNet04s2fPnnj8+DFmzJiB5ORkNGjQAAcOHICHhwcAIDk5WWnNmwEDBiAjIwNLlizBt99+i6pVq6JNmzb48ccf9fURNFK4gB8ALthHRESkI9x+oRy5ubkhKSkJrq6uSExMLNdrExERVWaVavsFIiIiIm1ickNERESSwuSGiIiIJIXJDREREUkKkxsiIiKSFL0+Cm5ogoODkZ6eXu5PaRERERkSJjc6lpGRodgvKzg4WN/hEBERSR6HpXTM19cXtra28PX11XcoREREBoHJDREREUkKkxsiIiKSFCY3REREJClMboiIiEhSmNwQERGRpDC5ISIiIklhckNERESSwuSGiIiIJIXJDREREUkKt1/QsT179iA3NxdyuVzfoRARERkEJjc65ufnp+8QiIiIDAqHpYiIiEhSmNwQERGRpHBYSsd+//13ZGdnw8LCAp999pm+wyEiIpI8Jjc6FhQUhKSkJLi6uiIxMVHf4RAREUkeh6V0KDIyEklJSfoOg4iIyKAwudGhkJAQxZ+rVKmix0iIiIgMB5MbHcrIyFD8eebMmXqMhIiIyHAwuSkHrq6u6N69u77DICIiMghMboiIiEhSmNwQERGRpDC5ISIiIklhcqND1tbWqFKlCqytrfUdChERkcHgIn46dP36dX2HQEREZHDYc0NERESSUqbkJi8vD3/++SdWrFihWMvl/v37yMzM1GpwRERERJrSOLm5e/cu3n33XXTt2hWjRo3Co0ePAABz587FxIkTtR5gZRUZGYkPP/wQd+7c0XcoREREBkXj5GbcuHHw9/fH06dPYWFhoSjv1q0bjhw5otXgKrOQkBDk5+djyJAhmDFjhr7DISIiMhgaTyg+ffo0zpw5A7lcrlTu4eHBTSJfk5GRoZhQHBQUpOdoiIiIDIfGPTcFBQXIz89XKU9MTOTmkEXg1gtERETlS+Pkpl27dli4cKHiWCaTITMzE6GhoejYsaM2YyMiIiLSmMbDUj/99BNat26NevXq4cWLF+jduzdu3boFe3t7bNmyRRcxEhEREalN4+TGxcUFly5dwtatWxETE4OCggIMHjwYffr0UZpgTERERKQPGic3J0+eRPPmzTFw4EAMHDhQUZ6Xl4eTJ0/io48+0mqARERERJrQeM5N69at8eTJE5XytLQ0tG7dWitBEREREZWVxsmNEAIymUyl/PHjx7CystJKUERERERlpfaw1BdffAHg1dNRAwYMgJmZmeK9/Px8XL58Gc2bN9d+hJVUp06d8OTJE9jZ2ek7FCIiIoOidnJja2sL4FXPTZUqVZQmD8vlcjRr1gxDhw7VfoSV1IoVK/QdAhERkUFSO7lZt24dAMDT0xMTJ07kEBQRERFVSBrPuQkNDWViU4rIyEi0adNG32EQEREZJI0fBQeA7du347fffkNCQgJyc3OV3rt48aJWAqvMQkJCkJGRgTt37sDT01Pf4RARERkUjXtuFi1ahIEDB8LBwQGxsbFo0qQJqlevjtu3b6NDhw66iLHSycjIQFJSErp27arvUIiIiAyOxsnNsmXLsHLlSixZsgRyuRyTJ09GVFQUxo4di7S0NF3EWKlERkYqdkd//PixnqMhIiIyPBonNwkJCYpHvi0sLJCRkQEA6Nu3L/eWwqshqULcJZ2IiKj8aZzcODk5KXokPDw88N///hcAEB8fDyGEdqOrhAqTPQCYOXOmHiMhIiIyTBonN23atMG+ffsAAIMHD8aECRPQrl079OzZE926ddN6gJWVq6srunfvru8wiIiIDI7GT0utXLkSBQUFAICgoCDY2dnh9OnT6Ny5M4KCgrQeIBEREZEmNE5ujIyMYGT0fx0+PXr0QI8ePQAASUlJcHV11V50RERERBrSeFiqKCkpKRgzZgzq1Kmjcdtly5bBy8sL5ubm8PPzw6lTp0qsn5OTg2nTpsHDwwNmZmaoXbs21q5dW9bQiYiISGLU7rl59uwZRo0ahcOHD8PU1BRTpkzB6NGjMX36dMyfPx/169fXOMnYtm0bxo8fj2XLlqFFixZYsWIFOnTogGvXrqFmzZpFtunRowcePHiANWvWoE6dOnj48CHy8vI0uq4uzZ07F1lZWbC0tNR3KERERAZJJtR8xGnkyJHYt28fevbsiYMHDyIuLg6BgYF48eIFQkND0apVK40v3rRpUzRu3BgRERGKMl9fX3z++eeYM2eOSv2DBw+iV69euH37dpl3205PT4etrS3S0tJgY2NTpnMQERFR+dLk+1vtYan9+/dj3bp1mD9/Pvbu3QshBLy9vXH06NEyJTa5ubmIiYlB+/btlcrbt2+Ps2fPFtlm79698Pf3x9y5c+Hq6gpvb29MnDgR2dnZxV4nJycH6enpSi8iIiKSLrWHpe7fv4969eoBAGrVqgVzc3MMGTKkzBdOTU1Ffn4+HB0dlcodHR2RkpJSZJvbt2/j9OnTMDc3x65du5CamoqRI0fiyZMnxQ6JzZkzB2FhYWWOk4iIiCoXtXtuCgoKYGpqqjg2NjbWyu7gMplM6VgIoVL2egwymQybNm1CkyZN0LFjR4SHh2P9+vXF9t5MnToVaWlpite9e/feOuaS3LhxA1evXsWNGzd0eh0iIiIqmto9N0IIDBgwAGZmZgCAFy9eICgoSCXB2blzp1rns7e3h7GxsUovzcOHD1V6cwo5OzvD1dUVtra2ijJfX18IIZCYmIh33nlHpY2ZmZki5vLwySefKB6JT0xMLLfrEhER0Stq99z0798fDg4OsLW1ha2tLb755hu4uLgojgtf6pLL5fDz80NUVJRSeVRUlGLvqje1aNEC9+/fR2ZmpqLs5s2bMDIygpubm9rXJiIiIulSu+dm3bp1Wr94cHAw+vbtC39/fwQEBGDlypVISEhQrHQ8depUJCUlYcOGDQCA3r17Y+bMmRg4cCDCwsKQmpqKSZMmYdCgQbCwsNB6fERERFT5aLxCsTb17NkTjx8/xowZM5CcnIwGDRrgwIED8PDwAAAkJycjISFBUd/a2hpRUVEYM2YM/P39Ub16dfTo0QPff/+9vj4CERERVTBqr3MjFbpe58bNzY1zboiIiLRMJ+vcEBEREVUGTG6IiIhIUpjcEBERkaSUKbnZuHEjWrRoARcXF9y9excAsHDhQuzZs0erwRERERFpSuPkJiIiAsHBwejYsSOePXuG/Px8AEDVqlWxcOFCbcdHREREpBGNk5vFixdj1apVmDZtGoyNjRXl/v7+uHLlilaDq4wuXLiAe/fu4cKFC/oOhYiIyCBpvM5NfHw8GjVqpFJuZmaG58+fayWoyszZ2VnfIRARERk0jXtuvLy8cOnSJZXyP/74Q7FrOBEREZG+aNxzM2nSJIwaNQovXryAEALnz5/Hli1bMGfOHKxevVoXMRIRERGpTePkZuDAgcjLy8PkyZORlZWF3r17w9XVFT///DN69eqlixgrlZUrVyIzMxPW1tYYNmyYvsMhIiIyOG+1/UJqaioKCgrg4OCgzZh0itsvEBERVT463X4hLCwM//77LwDA3t6+UiU2REREJH0aJzc7duyAt7c3mjVrhiVLluDRo0e6iIuIiIioTDRObi5fvozLly+jTZs2CA8Ph6urKzp27IjNmzcjKytLFzESERERqa1M2y/Ur18fs2fPxu3bt3Hs2DF4eXlh/PjxcHJy0nZ8RERERBp5640zraysYGFhAblcjpcvX2ojJiIiIqIyK1NyEx8fj1mzZqFevXrw9/fHxYsXMX36dKSkpGg7PiIiIiKNaLzOTUBAAM6fP493330XAwcOVKxzQ0RERFQRaJzctG7dGqtXr0b9+vV1EQ8RERHRW9E4uZk9e7Yu4pAMb29v2NrawtHRUd+hEBERGSS1kpvg4GDMnDkTVlZWCA4OLrFueHi4VgKrrI4eParvEIiIiAyaWslNbGys4kmo2NhYnQZERERE9Dbeam+pykjXe0sRERGR9ul0b6lBgwYhIyNDpfz58+cYNGiQpqcjIiIi0iqNe26MjY2RnJyssmFmamoqnJyckJeXp9UAtU3XPTd9+vRBamoq7O3tsWnTJq2fn4iIyBBp8v2t9tNS6enpEEJACIGMjAyYm5sr3svPz8eBAwe4QziAEydOICkpiWv/EBER6YnayU3VqlUhk8kgk8ng7e2t8r5MJkNYWJhWgyMiIiLSlNrJzbFjxyCEQJs2bbBjxw7Y2dkp3pPL5fDw8ICLi4tOgiQiIiJSl9rJTatWrQC82leqZs2akMlkOguKiIiIqKzUSm4uX76MBg0awMjICGlpabhy5UqxdRs2bKi14IiIiIg0pVZy8/777yMlJQUODg54//33IZPJUNRDVjKZDPn5+VoPkoiIiEhdaiU38fHxqFGjhuLPRERERBWVWsmNh4dHkX8mIiIiqmg0XqH4l19+wf79+xXHkydPRtWqVdG8eXPcvXtXq8ERERERaUrj5Gb27NmwsLAAAJw7dw5LlizB3LlzYW9vjwkTJmg9wMpm6NChmDBhAoYOHarvUIiIiAySxtsvWFpa4vr166hZsya+++47JCcnY8OGDbh69So+/vhjPHr0SFexagU3ziQiIqp8dLpxprW1NR4/fgwAOHz4MNq2bQsAMDc3R3Z2dhnCJSIiItIetRfxK9SuXTsMGTIEjRo1ws2bN9GpUycAwNWrV+Hp6ant+IiIiIg0onHPzdKlSxEQEIBHjx5hx44dqF69OgAgJiYGX3/9tdYDJCIiItKExnNuKjtdz7lxc3NT7AqemJio9fMTEREZIk2+vzUelgKAZ8+eYc2aNYiLi4NMJoOvry8GDx4MW1vbMgVMREREpC0aD0tFR0ejdu3a+Omnn/DkyROkpqbip59+Qu3atXHx4kVdxEhERESkNo17biZMmIAuXbpg1apVMDF51TwvLw9DhgzB+PHjcfLkSa0HSURERKQujZOb6OhopcQGAExMTDB58mT4+/trNTgiIiIiTWk8LGVjY4OEhASV8nv37qFKlSpaCYqIiIiorDRObnr27InBgwdj27ZtuHfvHhITE7F161YMGTKEj4ITERGR3mk8LDV//nzIZDL069cPeXl5AABTU1OMGDECP/zwg9YDJCIiItJEmde5ycrKwr///gshBOrUqQNLS0ttx6YTXOeGiIio8tHJ3lJZWVkYNWoUXF1d4eDggCFDhsDZ2RkNGzasNIkNERERSZ/aw1KhoaFYv349+vTpA3Nzc2zZsgUjRoxAZGSkLuOrdH799Vfk5OTAzMxM36EQEREZJLWHpWrXro1Zs2ahV69eAIDz58+jRYsWePHiBYyNjXUapDbpeliKiIiItE8nw1L37t1Dy5YtFcdNmjSBiYkJ7t+/X/ZIiYiIiLRM7eQmPz8fcrlcqczExETxxBQRERFRRaD2nBshBAYMGKA0l+TFixcICgqClZWVomznzp3ajbCSOX78uGLOzccff6zvcIiIiAyO2slN//79Vcq++eYbrQYjBd988w0fBSciItIjtZObdevW6TIOIiIiIq3QePsFbVu2bBm8vLxgbm4OPz8/nDp1Sq12Z86cgYmJCd5//33dBkhERESVil6Tm23btmH8+PGYNm0aYmNj0bJlS3To0KHIjTlfl5aWhn79+uGTTz4pp0iJiIiostBrchMeHo7BgwdjyJAh8PX1xcKFC+Hu7o6IiIgS2w0fPhy9e/dGQEBAOUVKRERElYXekpvc3FzExMSgffv2SuXt27fH2bNni223bt06/PvvvwgNDdV1iERERFQJabwruLakpqYiPz8fjo6OSuWOjo5ISUkpss2tW7cwZcoUnDp1CiYm6oWek5ODnJwcxXF6enrZgyYiIqIKr0w9Nxs3bkSLFi3g4uKCu3fvAgAWLlyIPXv2aHwumUymdCyEUCkDXi0i2Lt3b4SFhcHb21vt88+ZMwe2traKl7u7u8YxEhERUeWhcXITERGB4OBgdOzYEc+ePUN+fj4AoGrVqli4cKHa57G3t4exsbFKL83Dhw9VenMAICMjA9HR0Rg9ejRMTExgYmKCGTNm4K+//oKJiQmOHj1a5HWmTp2KtLQ0xevevXvqf1giIiKqdDRObhYvXoxVq1Zh2rRpShtm+vv748qVK2qfRy6Xw8/PD1FRUUrlUVFRaN68uUp9GxsbXLlyBZcuXVK8goKCULduXVy6dAlNmzYt8jpmZmawsbFRehEREZF0aTznJj4+Ho0aNVIpNzMzw/PnzzU6V3BwMPr27Qt/f38EBARg5cqVSEhIQFBQEIBXvS5JSUnYsGEDjIyM0KBBA6X2Dg4OMDc3VynXJ65KTEREpF8aJzdeXl64dOkSPDw8lMr/+OMP1KtXT6Nz9ezZE48fP8aMGTOQnJyMBg0a4MCBA4pzJycnl7rmDREREdHrZEIIoUmDdevW4T//+Q8WLFiAwYMHY/Xq1fj3338xZ84crF69Gr169dJVrFqRnp4OW1tbpKWlcYiKiIioktDk+1vjnpuBAwciLy8PkydPRlZWFnr37g1XV1f8/PPPFT6xISIiIunTuOfmdampqSgoKICDg4M2Y9IpXffchIWFIS0tDba2tlxokIiISEs0+f5+q+SmMtJ1cuPm5oakpCS4urpycjEREZGW6HRYysvLq8hF9grdvn1b01MSERERaY3Gyc348eOVjl++fInY2FgcPHgQkyZN0lZcRERERGWicXIzbty4IsuXLl2K6Ojotw6IiIiI6G1obVfwDh06YMeOHdo6HREREVGZaC252b59O+zs7LR1OiIiIqIy0XhYqlGjRkoTioUQSElJwaNHj7Bs2TKtBkdERESkKY2Tm88//1zp2MjICDVq1MDHH38MHx8fbcVFREREVCYaJTd5eXnw9PREYGAgnJycdBUTERERUZlplNyYmJhgxIgRiIuL01U8lV6rVq2QmpoKe3t7fYdCRERkkDQelmratCliY2NVdgWnVzZt2qTvEIiIiAyaxsnNyJEj8e233yIxMRF+fn6wsrJSer9hw4ZaC46IiIhIU2rvLTVo0CAsXLgQVatWVT2JTAYhBGQyGfLz87Udo1bpem8pIiIi0j6dbJxpbGyM5ORkZGdnl1ivog9X6TK5adOmDR48eABHR0ccPXpUq+cmIiIyZDrZOLMwB6royYs+3bx5E0lJSUhLS9N3KERERAZLoxWKS9oNnIiIiKgi0GhCsbe3d6kJzpMnT94qICIiIqK3oVFyExYWBltbW13FQkRERPTWNEpuevXqBQcHB13FQkRERPTW1J5zw/k2REREVBmondyo+cQ4ERERkV6pPSxVUFCgyziIiIiItEKjR8GJiIiIKjqN95ai4oWEhCAzMxPW1tb6DoWIiMhgMbnRomHDhuk7BCIiIoPHYSkiIiKSFCY3REREJCkcltKi5ORk5Ofnw9jYGM7OzvoOh4iIyCCx50aLPvjgA7i7u+ODDz7QdyhEREQGi8kNERERSQqTGyIiIpIUJjdEREQkKUxuiIiISFKY3BAREZGkMLkhIiIiSWFyQ0RERJLC5IaIiIgkhckNERERSQq3X9CiI0eOIC8vDyYmvK1ERET6wm9hLapbt66+QyAiIjJ4HJYiIiIiSWFyQ0RERJLCYSkt2rx5M7KysmBpaYnevXvrOxwiIiKDJBNCCH0HUZ7S09Nha2uLtLQ02NjYaPXcbm5uSEpKgqurKxITE7V6biIiIkOmyfc3h6WIiIhIUpjcEBERkaQwuSEiIiJJYXJDREREksLkhoiIiCSFyQ0RERFJCpMbIiIikhQmN0RERCQpXKFYi5ycnJT+S0REROWPyY0WRUdH6zsEIiIig6f3Yally5bBy8sL5ubm8PPzw6lTp4qtu3PnTrRr1w41atSAjY0NAgICcOjQoXKMloiIiCo6vSY327Ztw/jx4zFt2jTExsaiZcuW6NChAxISEoqsf/LkSbRr1w4HDhxATEwMWrdujc6dOyM2NracIyciIqKKSq8bZzZt2hSNGzdGRESEoszX1xeff/455syZo9Y56tevj549eyIkJESt+rrcOJOIiIh0o1JsnJmbm4uYmBi0b99eqbx9+/Y4e/asWucoKChARkYG7OzsdBGixoYPH46vvvoKw4cP13coREREBktvE4pTU1ORn58PR0dHpXJHR0ekpKSodY4FCxbg+fPn6NGjR7F1cnJykJOTozhOT08vW8Bq2L9/P5KSkuDq6qqzaxAREVHJ9D6hWCaTKR0LIVTKirJlyxZMnz4d27Ztg4ODQ7H15syZA1tbW8XL3d39rWMmIiKiiktvyY29vT2MjY1VemkePnyo0pvzpm3btmHw4MH47bff0LZt2xLrTp06FWlpaYrXvXv33jp2IiIiqrj0ltzI5XL4+fkhKipKqTwqKgrNmzcvtt2WLVswYMAAbN68GZ06dSr1OmZmZrCxsVF6ERERkXTpdRG/4OBg9O3bF/7+/ggICMDKlSuRkJCAoKAgAK96XZKSkrBhwwYArxKbfv364eeff0azZs0UvT4WFhawtbXV2+cgIiKiikOvyU3Pnj3x+PFjzJgxA8nJyWjQoAEOHDgADw8PAEBycrLSmjcrVqxAXl4eRo0ahVGjRinK+/fvj/Xr15d3+ERERFQB6XWdG33Q5To3bm5uiqelEhMTtXpuIiIiQ1Yp1rkhIiIi0gUmN0RERCQp3BVci77++ms8ffoU1apV03coREREBovJjRbNmzdP3yEQEREZPA5LERERkaQwuSEiIiJJYXJDREREksLkRot8fHxgY2MDHx8ffYdCRERksJjcaFFmZiYyMjKQmZmp71CIiIgMFpMbIiIikhQmN0RERCQpTG6IiIhIUpjcEBERkaQwuSEiIiJJYXJDREREksLkhoiIiCSFyQ0RERFJCncF16Lly5cjOzsbFhYW+g6FiIjIYDG50aLPPvtM3yEQEREZPA5LERERkaQwuSEiIiJJ4bCUFsXExCA3NxdyuRx+fn76DoeIiMggMbnRoq5duyIpKQmurq5ITEzUdzhEREQGicNSREREJClMboiIiEhSmNwQERGRpDC5ISIiIklhckNERESSwuSGiIiIJIXJDREREUkKkxsiIiKSFCY3REREJClcoViL4uLiIISATCbTdyhEREQGi8mNFlWpUkXfIRARERk8DksRERGRpDC5ISIiIknhsJQWhYeHIz09HTY2NggODtZ3OERERAZJJoQQ+g6iPKWnp8PW1hZpaWmwsbHR6rnd3NyQlJQEV1dXJCYmavXcREREhkyT728OSxEREZGkMLkhIiIiSWFyQ0RERJLC5IaIiIgkhckNERERSQqTGyIiIpIUJjdEREQkKVzEj4hIQoQQyMvLQ35+vr5DIdKYqakpjI2N3/o8TG60qHHjxnB3d0eNGjX0HQoRGaDc3FwkJycjKytL36EQlYlMJoObmxusra3f6jxMbrRo7969+g6BiAxUQUEB4uPjYWxsDBcXF8jlcshkMn2HRaQ2IQQePXqExMREvPPOO2/Vg8PkhohIAnJzc1FQUAB3d3dYWlrqOxyiMqlRowbu3LmDly9fvlVywwnFREQSYmTEf9ap8tJWbyP/LyAiIiJJ4bCUFnXp0gWPHj1CjRo1OP+GiIhIT9hzo0UXL17Ef//7X1y8eFHfoRARURkdPXoUPj4+KCgo0HcoktO9e3eEh4fr/DpMboiISK8GDBgAmUwGmUwGExMT1KxZEyNGjMDTp09V6p49exYdO3ZEtWrVYG5ujnfffRcLFiwocl2fY8eOoWPHjqhevTosLS1Rr149fPvtt0hKSioxnsmTJ2PatGmSnr+0bNkyeHl5wdzcHH5+fjh16lSpbZYuXQpfX19YWFigbt262LBhg0qdZ8+eYdSoUXB2doa5uTl8fX1x4MABxfshISGYNWsW0tPTtfp53iTdnxwREVUan376KZKTk3Hnzh2sXr0a+/btw8iRI5Xq7Nq1C61atYKbmxuOHTuG69evY9y4cZg1axZ69eoFIYSi7ooVK9C2bVs4OTlhx44duHbtGpYvX460tDQsWLCg2DjOnj2LW7du4auvvnqrz5Obm/tW7XVp27ZtGD9+PKZNm4bY2Fi0bNkSHTp0QEJCQrFtIiIiMHXqVEyfPh1Xr15FWFgYRo0ahX379inq5Obmol27drhz5w62b9+OGzduYNWqVXB1dVXUadiwITw9PbFp0yadfkYIA5OWliYAiLS0NK2f29XVVQAQrq6uWj83EVFJsrOzxbVr10R2dra+Q9FY//79RdeuXZXKgoODhZ2dneI4MzNTVK9eXXzxxRcq7ffu3SsAiK1btwohhLh3756Qy+Vi/PjxRV7v6dOnxcYyZswY0b17d6Wyf/75R3Tp0kU4ODgIKysr4e/vL6KiopTqeHh4iJkzZ4r+/fsLGxsb0a9fPyGEEGfOnBEtW7YU5ubmws3NTYwZM0ZkZmYq2m3cuFH4+fkJa2tr4ejoKL7++mvx4MGDYuPThiZNmoigoCClMh8fHzFlypRi2wQEBIiJEycqlY0bN060aNFCcRwRESFq1aolcnNzS7z+9OnTRcuWLYt8r6S/x5p8f7PnhohI4sLDw+Hm5lbqq0uXLiptu3TpolZbbc6juH37Ng4ePAhTU1NF2eHDh/H48WNMnDhRpX7nzp3h7e2NLVu2AAAiIyORm5uLyZMnF3n+qlWrFnvtkydPwt/fX6ksMzMTHTt2xJ9//onY2FgEBgaic+fOKj0d8+bNQ4MGDRATE4P//Oc/uHLlCgIDA/HFF1/g8uXL2LZtG06fPo3Ro0cr2uTm5mLmzJn466+/sHv3bsTHx2PAgAEl3p+goCBYW1uX+CquFyY3NxcxMTFo3769Unn79u1x9uzZYq+Zk5MDc3NzpTILCwucP38eL1++BPBqIduAgACMGjUKjo6OaNCgAWbPnq0yZNikSROcP38eOTk5JX7Ot1Jq+qNjS5cuFZ6ensLMzEw0btxYnDx5ssT6x48fF40bNxZmZmbCy8tLREREaHQ99twQkRSV9BtvaGioAFDqq1mzZiptmzVrplbb0NDQMsfev39/YWxsLKysrIS5ubninOHh4Yo6P/zwgwBQbK9Lly5dhK+vrxBCiBEjRggbG5syxWJrays2bNhQar169eqJxYsXK449PDzE559/rlSnb9++YtiwYUplp06dEkZGRsX2sJ0/f14AEBkZGcVe+8GDB+LWrVslvl6+fFlk26SkJAFAnDlzRql81qxZwtvbu9hrTp06VTg5OYno6GhRUFAgLly4IBwcHAQAcf/+fSGEEHXr1hVmZmZi0KBBIjo6WmzZskXY2dmJsLAwpXP99ddfAoC4c+eOynW01XOj10fBC8f9li1bhhYtWmDFihXo0KEDrl27hpo1a6rUj4+PR8eOHTF06FD8+uuvOHPmDEaOHIkaNWrgyy+/1MMnICKq+GxsbJTmPRSnqH3xatSooVZbGxubMsVWqHXr1oiIiEBWVhZWr16NmzdvYsyYMSr1xGvzat4sL1wA7vU/ayo7O1ulh+L58+cICwvD77//jvv37yMvLw/Z2dkqvSNv9vjExMTgn3/+UZpfIoRQbJXh6+uL2NhYTJ8+HZcuXcKTJ08UT2glJCSgXr16Rcbo4OAABweHMn2+Qm/en9Lu2X/+8x+kpKSgWbNmEELA0dERAwYMwNy5cxUrCRcUFMDBwQErV66EsbEx/Pz8cP/+fcybNw8hISGKc1lYWACATvdA02tyEx4ejsGDB2PIkCEAgIULF+LQoUOIiIjAnDlzVOovX74cNWvWxMKFCwEAvr6+iI6Oxvz585ncEBEVIzg4GMHBwWVqW15rdllZWaFOnToAgEWLFqF169YICwvDzJkzAQDe3t4AgLi4ODRv3lyl/fXr1xXJgLe3N9LS0pCcnAxnZ2eN4rC3t1d5SmvSpEk4dOgQ5s+fjzp16sDCwgLdu3dXmTRsZWWldFxQUIDhw4dj7NixKtepWbMmnj9/jvbt26N9+/b49ddfUaNGDSQkJCAwMLDECclBQUH49ddfS/wcxXUS2Nvbw9jYGCkpKUrlDx8+hKOjY7Hns7CwwNq1a7FixQo8ePAAzs7OWLlyJapUqQJ7e3sAgLOzs8qu3r6+vkhJSUFubi7kcjkA4MmTJwCKTqa1RW9zbsoy7nfu3DmV+oGBgYiOjlaM+b0pJycH6enpSi8iIqrYQkNDMX/+fNy/fx/Aq+8GOzu7Ip902rt3L27duoWvv/4awKu1VORyOebOnVvkuZ89e1bsdRs1aoRr164plZ06dQoDBgxAt27d8O6778LJyQl37twp9TM0btwYV69eRZ06dVRecrkc169fR2pqKn744Qe0bNkSPj4+ePjwYannnTFjBi5dulTiy8XFpci2crkcfn5+iIqKUiqPiooqMml8k6mpKdzc3GBsbIytW7fis88+Uzwy36JFC/zzzz9K6wPdvHkTzs7OisQGAP7++2+4ubkpkiJd0FvPTWpqKvLz81UyRUdHR5WMslBKSkqR9fPy8pCamlpkhj5nzhyEhYVpL/ASBAcHIz09/a27Z4mIDN3HH3+M+vXrY/bs2ViyZAmsrKywYsUK9OrVC8OGDcPo0aNhY2ODI0eOYNKkSejevTt69OgBAHB3d8dPP/2E0aNHIz09Hf369YOnpycSExOxYcMGWFtbF/s4eGBgIH755Relsjp16mDnzp3o3LkzZDIZ/vOf/6i1wN93332HZs2aYdSoURg6dCisrKwQFxeHqKgoLF68GDVr1oRcLsfixYsRFBSEv//+W9FTVZK3HZYKDg5G37594e/vj4CAAKxcuRIJCQkICgpS1Jk6dSqSkpIUa9ncvHkT58+fR9OmTfH06VOEh4fj77//VrpXI0aMwOLFizFu3DiMGTMGt27dwuzZs1V6rk6dOqXSUaF1pc7K0ZHCSU1nz55VKv/+++9F3bp1i2zzzjvviNmzZyuVnT59WgAQycnJRbZ58eKFSEtLU7zu3bunswnFRET6IrVHwYUQYtOmTUIul4uEhARF2cmTJ8Wnn34qbG1thVwuF/Xq1RPz588XeXl5Ku2joqJEYGCgqFatmjA3Nxc+Pj5i4sSJigmwRXny5ImwsLAQ169fV5TFx8eL1q1bCwsLC+Hu7i6WLFkiWrVqJcaNG6eo4+HhIX766SeV850/f160a9dOWFtbCysrK9GwYUMxa9YsxfubN29WPFQTEBCgeKw9Nja25Jv2lpYuXSo8PDyEXC4XjRs3FidOnFB6v3///qJVq1aK42vXron3339fWFhYCBsbG9G1a1ele1To7NmzomnTpsLMzEzUqlVLzJo1S+lnk52dLWxsbMS5c+eKjEtbE4plQhQzO0vHcnNzYWlpicjISHTr1k1RPm7cOFy6dAknTpxQafPRRx+hUaNG+PnnnxVlu3btQo8ePZCVlaX02GBx0tPTYWtri7S0NPawEJFkvHjxAvHx8YpVZ6nsJk+ejLS0NKxYsULfoUjO0qVLsWfPHhw+fLjI90v6e6zJ97fe5tyUZdwvICBApf7hw4fh7++vVmJDRERUmmnTpsHDw6PILR3o7ZiammLx4sU6v45eF/ELDg7G6tWrsXbtWsTFxWHChAlK435Tp05Fv379FPWDgoJw9+5dBAcHIy4uDmvXrsWaNWuKXNSJiIioLGxtbfE///M/Sk/9kHYMGzYMdevW1fl19PooeM+ePfH48WPMmDEDycnJaNCgAQ4cOAAPDw8AQHJystI6Al5eXjhw4AAmTJiApUuXwsXFBYsWLeJj4ERERKSgtzk3+sI5N0QkRZxzQ1JQ6efcEBGR9hnY76skMdr6+8vkhohIAgofqtDlkvZEula4MvPbznfS65wbIiLSDmNjY1StWlWxwq2lpWWZ91ci0oeCggI8evQIlpaWMDF5u/SEyQ0RkUQ4OTkBgFpL+BNVREZGRqhZs+ZbJ+ZMboiIJEImk8HZ2RkODg7F7rdHVJHJ5XLFXlVvg8kNEZHEGBsbc40WMmicUExERESSwuSGiIiIJIXJDREREUmKwc25KVwgKD09Xc+REBERkboKv7fVWejP4JKbjIwMAIC7u7ueIyEiIiJNZWRkwNbWtsQ6Bre3VEFBAe7fv48qVapofYGr9PR0uLu74969e9y3Sod4n8sH73P54H0uP7zX5UNX91kIgYyMDLi4uJT6uLjB9dwYGRnBzc1Np9ewsbHh/zjlgPe5fPA+lw/e5/LDe10+dHGfS+uxKcQJxURERCQpTG6IiIhIUpjcaJGZmRlCQ0NhZmam71Akjfe5fPA+lw/e5/LDe10+KsJ9NrgJxURERCRt7LkhIiIiSWFyQ0RERJLC5IaIiIgkhckNERERSQqTGw0tW7YMXl5eMDc3h5+fH06dOlVi/RMnTsDPzw/m5uaoVasWli9fXk6RVm6a3OedO3eiXbt2qFGjBmxsbBAQEIBDhw6VY7SVl6Z/nwudOXMGJiYmeP/993UboERoep9zcnIwbdo0eHh4wMzMDLVr18batWvLKdrKS9P7vGnTJrz33nuwtLSEs7MzBg4ciMePH5dTtJXTyZMn0blzZ7i4uEAmk2H37t2lttHL96AgtW3dulWYmpqKVatWiWvXrolx48YJKysrcffu3SLr3759W1haWopx48aJa9euiVWrVglTU1Oxffv2co68ctH0Po8bN078+OOP4vz58+LmzZti6tSpwtTUVFy8eLGcI69cNL3PhZ49eyZq1aol2rdvL957773yCbYSK8t97tKli2jatKmIiooS8fHx4n//93/FmTNnyjHqykfT+3zq1ClhZGQkfv75Z3H79m1x6tQpUb9+ffH555+Xc+SVy4EDB8S0adPEjh07BACxa9euEuvr63uQyY0GmjRpIoKCgpTKfHx8xJQpU4qsP3nyZOHj46NUNnz4cNGsWTOdxSgFmt7notSrV0+EhYVpOzRJKet97tmzp/h//+//idDQUCY3atD0Pv/xxx/C1tZWPH78uDzCkwxN7/O8efNErVq1lMoWLVok3NzcdBaj1KiT3Ojre5DDUmrKzc1FTEwM2rdvr1Tevn17nD17tsg2586dU6kfGBiI6OhovHz5UmexVmZluc9vKigoQEZGBuzs7HQRoiSU9T6vW7cO//77L0JDQ3UdoiSU5T7v3bsX/v7+mDt3LlxdXeHt7Y2JEyciOzu7PEKulMpyn5s3b47ExEQcOHAAQgg8ePAA27dvR6dOncojZIOhr+9Bg9s4s6xSU1ORn58PR0dHpXJHR0ekpKQU2SYlJaXI+nl5eUhNTYWzs7PO4q2synKf37RgwQI8f/4cPXr00EWIklCW+3zr1i1MmTIFp06dgokJ/+lQR1nu8+3bt3H69GmYm5tj165dSE1NxciRI/HkyRPOuylGWe5z8+bNsWnTJvTs2RMvXrxAXl4eunTpgsWLF5dHyAZDX9+D7LnRkEwmUzoWQqiUlVa/qHJSpul9LrRlyxZMnz4d27Ztg4ODg67Ckwx173N+fj569+6NsLAweHt7l1d4kqHJ3+eCggLIZDJs2rQJTZo0QceOHREeHo7169ez96YUmtzna9euYezYsQgJCUFMTAwOHjyI+Ph4BAUFlUeoBkUf34P89UtN9vb2MDY2Vvkt4OHDhypZaSEnJ6ci65uYmKB69eo6i7UyK8t9LrRt2zYMHjwYkZGRaNu2rS7DrPQ0vc8ZGRmIjo5GbGwsRo8eDeDVl7AQAiYmJjh8+DDatGlTLrFXJmX5++zs7AxXV1fY2toqynx9fSGEQGJiIt555x2dxlwZleU+z5kzBy1atMCkSZMAAA0bNoSVlRVatmyJ77//nj3rWqKv70H23KhJLpfDz88PUVFRSuVRUVFo3rx5kW0CAgJU6h8+fBj+/v4wNTXVWayVWVnuM/Cqx2bAgAHYvHkzx8zVoOl9trGxwZUrV3Dp0iXFKygoCHXr1sWlS5fQtGnT8gq9UinL3+cWLVrg/v37yMzMVJTdvHkTRkZGcHNz02m8lVVZ7nNWVhaMjJS/Ao2NjQH8X88CvT29fQ/qdLqyxBQ+arhmzRpx7do1MX78eGFlZSXu3LkjhBBiypQpom/fvor6hY/ATZgwQVy7dk2sWbOGj4KrQdP7vHnzZmFiYiKWLl0qkpOTFa9nz57p6yNUCpre5zfxaSn1aHqfMzIyhJubm+jevbu4evWqOHHihHjnnXfEkCFD9PURKgVN7/O6deuEiYmJWLZsmfj333/F6dOnhb+/v2jSpIm+PkKlkJGRIWJjY0VsbKwAIMLDw0VsbKzikfuK8j3I5EZDS5cuFR4eHkIul4vGjRuLEydOKN7r37+/aNWqlVL948ePi0aNGgm5XC48PT1FREREOUdcOWlyn1u1aiUAqLz69+9f/oFXMpr+fX4dkxv1aXqf4+LiRNu2bYWFhYVwc3MTwcHBIisrq5yjrnw0vc+LFi0S9erVExYWFsLZ2Vn06dNHJCYmlnPUlcuxY8dK/Pe2onwPyoRg/xsRERFJB+fcEBERkaQwuSEiIiJJYXJDREREksLkhoiIiCSFyQ0RERFJCpMbIiIikhQmN0RERCQpTG6ISMn69etRtWpVfYdRZp6enli4cGGJdaZPn47333+/XOIhovLH5IZIggYMGACZTKby+ueff/QdGtavX68Uk7OzM3r06IH4+HitnP/ChQsYNmyY4lgmk2H37t1KdSZOnIgjR45o5XrFefNzOjo6onPnzrh69arG56nMySaRPjC5IZKoTz/9FMnJyUovLy8vfYcF4NVGnMnJybh//z42b96MS5cuoUuXLsjPz3/rc9eoUQOWlpYl1rG2ttbpjsSFXv+c+/fvx/Pnz9GpUyfk5ubq/NpEhozJDZFEmZmZwcnJSellbGyM8PBwvPvuu7CysoK7uztGjhyptAP1m/766y+0bt0aVapUgY2NDfz8/BAdHa14/+zZs/joo49gYWEBd3d3jB07Fs+fPy8xNplMBicnJzg7O6N169YIDQ3F33//rehZioiIQO3atSGXy1G3bl1s3LhRqf306dNRs2ZNmJmZwcXFBWPHjlW89/qwlKenJwCgW7dukMlkiuPXh6UOHToEc3NzPHv2TOkaY8eORatWrbT2Of39/TFhwgTcvXsXN27cUNQp6edx/PhxDBw4EGlpaYoeoOnTpwMAcnNzMXnyZLi6usLKygpNmzbF8ePHS4yHyFAwuSEyMEZGRli0aBH+/vtv/PLLLzh69CgmT55cbP0+ffrAzc0NFy5cQExMDKZMmQJTU1MAwJUrVxAYGIgvvvgCly9fxrZt23D69GmMHj1ao5gsLCwAAC9fvsSuXbswbtw4fPvtt/j7778xfPhwDBw4EMeOHQMAbN++HT/99BNWrFiBW7duYffu3Xj33XeLPO+FCxcAAOvWrUNycrLi+HVt27ZF1apVsWPHDkVZfn4+fvvtN/Tp00drn/PZs2fYvHkzACjuH1Dyz6N58+ZYuHChogcoOTkZEydOBAAMHDgQZ86cwdatW3H58mV89dVX+PTTT3Hr1i21YyKSLJ1vzUlE5a5///7C2NhYWFlZKV7du3cvsu5vv/0mqlevrjhet26dsLW1VRxXqVJFrF+/vsi2ffv2FcOGDVMqO3XqlDAyMhLZ2dlFtnnz/Pfu3RPNmjUTbm5uIicnRzRv3lwMHTpUqc1XX30lOnbsKIQQYsGCBcLb21vk5uYWeX4PDw/x008/KY4BiF27dinVeXNH87Fjx4o2bdoojg8dOiTkcrl48uTJW31OAMLKykpYWloqdk/u0qVLkfULlfbzEEKIf/75R8hkMpGUlKRU/sknn4ipU6eWeH4iQ2Ci39SKiHSldevWiIiIUBxbWVkBAI4dO4bZs2fj2rVrSE9PR15eHl68eIHnz58r6rwuODgYQ4YMwcaNG9G2bVt89dVXqF27NgAgJiYG//zzDzZt2qSoL4RAQUEB4uPj4evrW2RsaWlpsLa2hhACWVlZaNy4MXbu3Am5XI64uDilCcEA0KJFC/z8888AgK+++goLFy5ErVq18Omnn6Jjx47o3LkzTEzK/s9Znz59EBAQgPv378PFxQWbNm1Cx44dUa1atbf6nFWqVMHFixeRl5eHEydOYN68eVi+fLlSHU1/HgBw8eJFCCHg7e2tVJ6Tk1Muc4mIKjomN0QSZWVlhTp16iiV3b17Fx07dkRQUBBmzpwJOzs7nD59GoMHD8bLly+LPM/06dPRu3dv7N+/H3/88QdCQ0OxdetWdOvWDQUFBRg+fLjSnJdCNWvWLDa2wi99IyMjODo6qnyJy2QypWMhhKLM3d0dN27cQFRUFP7880+MHDkS8+bNw4kTJ5SGezTRpEkT1K5dG1u3bsWIESOwa9curFu3TvF+WT+nkZGR4mfg4+ODlJQU9OzZEydPngRQtp9HYTzGxsaIiYmBsbGx0nvW1tYafXYiKWJyQ2RAoqOjkZeXhwULFsDI6NWUu99++63Udt7e3vD29saECRPw9ddfY926dejWrRsaN26Mq1evqiRRpXn9S/9Nvr6+OH36NPr166coO3v2rFLviIWFBbp06YIuXbpg1KhR8PHxwZUrV9C4cWOV85mamqr1FFbv3r2xadMmuLm5wcjICJ06dVK8V9bP+aYJEyYgPDwcu3btQrdu3dT6ecjlcpX4GzVqhPz8fDx8+BAtW7Z8q5iIpIgTiokMSO3atZGXl4fFixfj9u3b2Lhxo8owyeuys7MxevRoHD9+HHfv3sWZM2dw4cIFRaLx3Xff4dy5cxg1ahQuXbqEW7duYe/evRgzZkyZY5w0aRLWr1+P5cuX49atWwgPD8fOnTsVE2nXr1+PNWvW4O+//1Z8BgsLC3h4eBR5Pk9PTxw5cgQpKSl4+vRpsdft06cPLl68iFmzZqF79+4wNzdXvKetz2ljY4MhQ4YgNDQUQgi1fh6enp7IzMzEkSNHkJqaiqysLHh7e6NPnz7o168fdu7cifj4eFy4cAE//vgjDhw4oFFMRJKkzwk/RKQb/fv3F127di3yvfDwcOHs7CwsLCxEYGCg2LBhgwAgnj59KoRQnsCak5MjevXqJdzd3YVcLhcuLi5i9OjRSpNoz58/L9q1ayesra2FlZWVaNiwoZg1a1axsRU1QfZNy5YtE7Vq1RKmpqbC29tbbNiwQfHerl27RNOmTYWNjY2wsrISzZo1E3/++afi/TcnFO/du1fUqVNHmJiYCA8PDyGE6oTiQh988IEAII4eParynrY+5927d4WJiYnYtm2bEKL0n4cQQgQFBYnq1asLACI0NFQIIURubq4ICQkRnp6ewtTUVDg5OYlu3bqJy5cvFxsTkaGQCSGEftMrIiIiIu3hsBQRERFJCpMbIiIikhQmN0RERCQpTG6IiIhIUpjcEBERkaQwuSEiIiJJYXJDREREksLkhoiIiCSFyQ0RERFJCpMbIiIikhQmN0RERCQpTG6IiIhIUv4/OCJg0nPZrkgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "0.9638276469108894"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predict_pro_list, AUC = get_roc_auc(model,test_X,test_label_list)\n",
    "AUC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bd559eaf",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "a998e05d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 统计详情\n",
    "evaluation_now = list(eval_model(test_label_list, predict_label_list, labelEncoder.classes_).iloc[1,[1,2,3]].values)\n",
    "evaluation_now.append(score_r)\n",
    "evaluation_now.append(AUC)\n",
    "\n",
    "evaluation_ls.append(evaluation_now)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "574ff22e",
   "metadata": {},
   "source": [
    "- 特征解释"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "e2002f7e",
   "metadata": {},
   "outputs": [],
   "source": [
    "import shap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "bebe12d6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GradientBoostingClassifier(n_estimators=290, random_state=0)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GradientBoostingClassifier</label><div class=\"sk-toggleable__content\"><pre>GradientBoostingClassifier(n_estimators=290, random_state=0)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "GradientBoostingClassifier(n_estimators=290, random_state=0)"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "5d812489",
   "metadata": {},
   "outputs": [],
   "source": [
    "explainer = shap.TreeExplainer(model)\n",
    "shap_values = explainer.shap_values(train_X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "750556fe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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eZVhNcXbP2cKq1REaMbBtl1pHJxG18YT8tBVlYwQMpk0KckHTCnb/YiEtMS+ma1IcraYp0+I3n/gMG3KGcqo7hWu1JOPUolRvdVfiXp8zhMxmLx2qCIAcdtCUlsa6YAW5sSbK23cwvXYFC0pmAmA6Vndoh564UZlRkArtQG5nhOm71nL6znfISrRRlVbERv04DLfv65Nlt3LdE2/jTbokSGeJPglngPxia6l96lVr3uGdokkks3IYGo1yzcLn2GmPJEqIDCKY9D2bMqKplbzOKA3hILZh8FbZcEra+h6prssaztiOrXRYOTi9IkHS0MlsaEd1XdSsTJPOrFQ9fnZHO3bQx/PxfFb/dC2f/Pzf8bhO15xdbTBMNEMDA5Tlgq3Qzx1F+MHL0AuPsLHo61vhH/OgIw5fPBnGyYg84sh1wBenXnPNNbz11lvcc889VFZWsnz5cm688UZGjBhxwI2ZPn06hmFw7733EggEGDFiBG1tbfzjH//Atm2uvvrqA162EOLYFkkqTnzYYW3T4W7Jx0PMht+9C3sS8G/fVXxrquLOs3rKaQo3tsM3f47XcigDcp9+m8eevIjzqxaTt2UnC0onsaw4vXv69c/X8Ll7pqJpGsm6GEtnPMuacBpNhelA6nayHmBzXiavVJRx8u5mzGabXbtaSS6sw/WNJl3v4NSGRZhmnLNuvIOmUGr5y4YNoThRyfi3+wbXipZaatXY7r+XF49kS9bw7r/L23YwvmU9lmngsR2y4y3dob03zdUxHAfHMChsb+KCzS9hqlSYHdJRQ8CXYB0ze/W4uyhcvMmesG3rGrlWK7vNvh1eZYmdAARsOH9XLZGGBq5e/BK2bbKdCgAsjH7BHaCkvZOGcOosgNdy2PuG6ZqCDbnlFNV09Hm8JRzoDu0Aoc4EsZCXeMhHcziN81YtY1hDHduKC/G4Lj2HMAagwFEoRwEKHQcXHfflTXRe9hDp877Rr52Hza5GmPE/UNea+vtXT8GzP4JPTjmszRJiXw74vJVpmsyePRvHcXj00UcZP348V1111YdqzLBhw/j5z39OLBbjW9/6Fueffz5f+tKXWLZsGX/605+YOXPmh1q+EOLY9dhGJaH9EPvDckVdpCcYT35+N5rldP8dtOJM2LKGd80y4oaHFYWj+8y/a2kLVe+lSlSq/7yRSE2Mpvz+vbPjGlop6IiwPTN1caQOuE6qBv30+gXkJ5p4auLx3aF9j93Zff9W6HhdhYfUQPwxj8mWzGF9ptmWMYyVxWNYNXI4m4cWYZkmtta31t9FY175GZQ3NpMWiTJz+5ru0L5HTqKZZLqD43XIpo7xrMDWenbBumYTy/KQ5kYYEakiZEfJsFuY3rGYfKsOGw+BBFz03hIueec9sE2SeLvnj+LDJXUYpUj1ezcGA6zNzyHNcRmVtHl6xszuaK+5ilCnRUZbkuZkDn69kz295QpIevr365XtamLSukpyWiPEvF7KmhqZvHNLv+n6lj1oKHR0usajf3M7KmkPMM9hcu8LPaEdwLLh548ftuYI8UE+VMFZOBzG6019cZx00kkYxoe/cCk7O5uxY8dy3XXXcfvtt/PDH/4Qv9/Pt771LdavX/+hl3+wNDc3k0j03HWls7OTjo6eHotkMklTU9+UUFNT875/19bWolTPTk/WIeuQdRy8ddREEIeYo6Ch6y6woVCIUIfTb5pwMkanL0jC9OHq/fcZkcbUe5qsSdVW76vytbgjQosvFdZdTUMBfidObjIV/DXV/+KFF0dP3usi5NTSs6lBw8EyzT715XvML58BQHswxJrh5WjK170cBaxPH0ebP4ORNbV8YtV6/LH+63bRKWtvY1JyHbnESJJLiWoj4MbIcGK4QQNX19kZLKQk0cAJbeuZ0baRnGSUdobTyVCSZJBBCxqKDjIxuqN6als6CGCjo9CImx7mjxjGzNoGSmwHXdNoCwZ4/rjRRLwe0jss/EkX01FYjp+FmdNYO7qElowgkaAX3e3fex+Op+r0x26uJr0jdcMAXX3wxc6q97uoA56e9/2wf5fUtPRrr1vdfNi/rz6qdRwpFNqgf451mlIDfLsNglKKG2+8kWXLllFWVkZVVRUPP/www4cP3+c8c+fO5a677trncJALFizgO9/5DnfeeScnnnhi9+Otra1ceuml5Ofn8/DDDx9Ic4UQx7i1jYrjHnRw5WLUQ2ZUFmy4xsC2bebMmcPUZ3Yx+YXqPtP8Z9wZlLXWMLl6I/+c8CmaQlndz3kCBtc8czKBDC/Nr1az8pyX2DKmgMaijH7remjSCAKuTkVbFM1x+PT8d1GaxmWVz+BzLVr9Qcb8z++pS8vsnufSxWu494kHCXjqCVsxUmUdKRY+2sjlqTFn4PbqhLIMg2XjR6L0nn6u3z59D9nRNsBmgzGanZ6hOKaGk5WkLj0PlOKCnS8zJNKz7cvTx7E0/zgu3rK6z3Y0mWnE7DA1mWGivlRHmN9JUBJrwmcnKEk00/fwRdFBGKertz2Kjw6CuF3T6F2/RbweNo4sYENBLi+N7VvCmt/ayXf/u7Dfa7q7LJvtpfnUZ2aQ0RbhxPc24rVSvePheILczkh3S4LE8JtxFo0axSfXLe1+XNH/YEvD6QpcOsZJZaS9881+6z5s/r0EPvPrvo99/2L43VcOT3uOUUr78qCn1dTcQ9iSI98B97g//vjjLFmyhK997Wv89re/RdO07tKZA/WPf/wDv9/fJ7QDZGZmMmPGDDZu3Nh9AasQQuyP8bka/zxfJ0uucT+ocgNgaHBGqcZ/PpsaWWSPFReU4F5/BrbPS9Qb4J1xx5PzvdOY8tOZMCSLT1cuojQYQzc0ckeEueh3kwhkpAJp9tnFjLrvRMZ2dFBY05q6oJRUMHy3JI9On5fytgjpaToXXJDBGecZZFgdLM+cAEBmPMo79/6MK997mzHVdVzx5gq+8uZi2rV8NvlmcvnlvyRq9HwYPCRIp4GlJflsyUrH0WB3eoiN5aV9QnsgGSctHgV0NhmjqTSHoikwLUW7npmaSNN4fug5vDLkdFZlTuC90PE4XoNPV8+jMxzD1Xp6qb126vdwvKduPm742BouxsBioBiser3GQRLk00ImHRgoNDRAI+5NnY3IjMbZ276OXWNeD3VZmShNozUzzEtnTmHl2GEUNbeR1yu0AyQMg5UlwxhTuQvVVQiz50cblwdeo6vpqju0a0MzCT09ax9rP0wuPh7uuBoKMyHkh+s/CT+//ANnE+JwOaCLU6uqqrjnnnsYO3Yss2bNwjRNrr/+eu666y7mzp3LrFmzDqgx9fX1OI6DUqrPlz+Abdt9/hVCiP31hTE6l4zW+NwzDv/eerhbc3Qanw0XjtAIeuB703WCnn2funZNHe3uy/D/8cv4gdO7nxkKXz+dHODz77Oukm+MoeQbYzgFcB1FzaYOttsevjDEz4gsDejpreeLl1B2lwXLtmG1fJqtj6xlS7XJZyy4oX4FdWkBts06lejbq5mxcBvfev41/j3yHC7d8CIGFjHCLPdMoyIa5w+TxlKVlqqfP7OpjbJE1wgySnHOqmXElSKGn3o9t097g51JYuE95Ts6WzPK6bRzOTExn8LG+u7p2sx0EvYQQEPTHXAhLZ7E0aO0BVPDTpbHqhiW2E6nVoC5VzlKmz9EWqwn6Gukbr60RxIdN6lhWC6FHZ2MrmtkY0FPW6fsqKM1I0RmW0/9WDzgoT6375kNV9fZMbSA5AIP/l7LdzSNTQVFDG2po27aWCquPA33TwsxhmUT+MPFGAV9r0tw1tdB0sGYVDzQ23z4fefC1I8QR4H9Du6u63b3rM+ePbt76McrrriCN954gwceeIDTTjuN8vLy/W5MeXk5lZWVvPLKK5x77rndj9fV1bF48WJKSkrIzMzc7+UKIcQeuqbxzGdNajoVL253+dF8RV30cLfqyJbmgSFpcM1Eje9N1/t1rHwUdEOjZGw6Je83kdcDJ4zGA4w5bzJjBpxoHMknlpP89XwCLTtYUVGOp10npyVC3A4STFp8e8U61mZn0uk1GdvUyo70DPI64zR6Te6ZOYHPbXwaAMuY3Kf7OtRpkVFbSXVOHso1KWjtwKc6KYzV92lBht1OMzGSBFlfVIxleylpaCczGic/2sYQKgnShtKirCwezrTdPUeZMfzUh7JIi9X13XSSJLpKfxKaiWErSra1EQ+bfGP3IurDISpzM6keVoSZH2J17jCKqpsprG2htL6JXTk5+AfoGAt1xnEdDV1P0OENEfd4aA77KY3VM+mHEwn+6KzUhF+bsc+3xRhbsM/nhBD7Z7+D+8MPP8yKFSu46aabqKio6H5c13V+9rOf8aUvfYnZs2czZ84cDMNg8+bNvPnmmwCsWrUKgOeff54VK1YAcP7551NUlBo/9+qrr2bhwoX89Kc/ZdmyZYwaNYqGhgaeeuopotEot9xyy4fdXiGEAKAorHH1RIOrJ8K/N7t85t+Dv6Po0awoBN+aqrGtTRG34fGNENtHhaMO1N5gkBf8eF0Q5r1kCqMu2Wu4v8oG0l7cTvTnm9lWlM9xTamLFiN+h39PnERbIMDU6u386/G7AejU0ghoHVjKx55yFk1zmNn8Lt6mJOu1EzEUtGcOXJiyMbeITdnDiHlSPezZTgvDG5sxsXhv6BDyOv2sDE9ieEsL7aRjYuNg4GCS095BEgNv1xCZOi7pNBMmjU7C+JSDVzk4CgoiTRiWQWFrJ1OqqthZu4tlY8Zi+Uzyky18qua/1NpjyNwaY0h9M68eP5GdRal7qRi2w+nvrqWc3bimRn68Gd2yMRMQ/up0fP9zxkF+Z4QQH2S/Lk7dvn07V155JaNGjeIvf/nLgKPIPPTQQ9x5553ccMMNXHPNNTz77LPceuut+1zm3heq7tixg7/+9a+sXr2a+vp6fD4f48aN46qrrpLhIIUQh8zXX7J5YPUHT3e0GpEJXz9O47szdPRePeZLax1mPDTwbuCMUnjji/tfUWlZFnPmzAFSHTIej+eA2ny4RLa3snt1O9njs/GUhHjk18sw3thMYdJihllFYXkIe9o4ktlZbH24jsbXqkl4FcsmlaOCATQU+fXtTF63DU1BmWcl+fHG7uU3+bJ4vOLiPiPYDK+sxvWZZNoR2nwBdmTm4Us6jK+vJj/S99ouFwjQQIg4LhohmjFIssh7KjE3A8NxcXQNx9QoTLQCOkGiBIjjaAYaGoZyCX59Gp0Lt6Jt2EzcTScWyMUX6WBbZhnry4aArvA5FoXtHUw+LYuyu89Ed2zwGGhhH0IcLEob/HDimvrHIWzJke+AR5URQoiPm0fW21zx38PdioMnYEBZBvzPDI1rJu57uN7LnnV4dGPfXUFpGF77osHIrP3vbT/ag/t+U4qWX72Afec85lQcz8q8csZU13LK1i2c0LoGR4c2nxePSlLvz2Fj+kjaA+kkzFT4LWvbjesa1KbngIJiJ0qzZWLpHvI6O5jQULPXCl18ZxVR+Pki1G//g76zDgsva82ZVHtyUZqGrhzKA23kfn8GjMileU0rm5e0UpmRxsgyg5OuH0mgPDPV/GgSvAaaaaDe3Yb9yLtEEz7Mr52Ab1wepv/DD/UsxPuR4D54EtyFEKKX7HtsWhIfPN2RKmCArsEXxmjcfZZO2PvBwdtViv9uU6yoh6KQYnyuzvGFYOgHViJzzAX3XpznV+M+tQJOrMC8YgbR7z+D9Zf5+Kw4OzOL8VlRhndU42g69YFsglYMTek4dhq1/+dsSv/nJMIlYV4+42l2V6bKt0rbmilta8F0XCwM6ktLOa3ysu51qpsfRv32GZJOmGZKiHrDZH9uBJn3X4CeIcMoiSOfBPfBk+AuhBC9vLjd4bwnB/e1eHIx/PJUneKQYuTfDu9X6YQceOtygyz/4a9HP5aD+750NiSIVHfimXYrWU5zn3HP28mnM5RJccct3Rf+du6O8Oqlr9JSa6Mpl7yOdjKaHJJ4GPXYmQy5dFjfFdS1wq5GmDQMBrjrqRBHMgnug/eh7pwqhBAfN58abnBxxcDP/eJkmJoPnx4OG67WeftLJqeV6ozINph94uELzGeUwuqrzSMitIuBhfN8FEzKoer2a9ieNpQYIWKEaSefpB4geOcFfUbrCZeE+MyCiznvmU+Q/onhJPyZtIwsoPSfZ/UP7QAFmTB9hIR2cVSSO6cOnvS4CyHEXlyl+OarDg+sAkeB34D/d6bO9ZPfv6/jwbUOs17Yv6/Uy0fD2Bz46YIDa+u0Anj9CwbpviNnhyY97u9v27stbHq7mdz6BobmKHK+PAF9r7HPhTiWuNrg71SrqwcPYUuOfHJoLoQQe9E1jfvOMbn7E4rWOOQOcjjEK8fqzHph/+4efc/ZOll+jRe2OyzsdQ3isHTY0T7wPKVp8KtTNc4frpEZkBOnR5vyGVmUz8gC9nFqRwgh9kGCuxBC7IOpa+QGBz/9/lzMaWjwzws0crqC94IrTNY1umxvg7PKNAIejd8tcZi9QBHtdV8cU4PXLtUZmS2BXQjxcXHknDE80klwF0KIj5gOxL5t4DH67qzG5eqM67kzPT843uAHx8PONsXdy1zSvPDd6foRVRYjhBDioyPBXQghDqLh6bB9HyUue1w4QusX2t/P0AyN358pY2kLIcSxTs61CiHEQTTvMoPS97nO8KRiuPcT8tUrhBA9tP34ObZJj7sQQhxEZekalV83qY0odrYr5lcpAiacVaqRHdAoCMmORwghxIGR4C6EEIdAYUijMKQxs+hwt0QIIcTHhZyvFUIIIYQQ4iggPe5CCCGEEOKwkTuiDp70uAshhBBCCHEUkOAuhBBCCCHEUUBKZYQQQhwWtq0AME05TS7EsU2+AwZLgrsQQoiPRGfE4Zm5dbw+v5OoboIGhq5xyklhvjorB69XTgILIcT7keAuhBDikHvoX03MfSVCyHHx6AaacgnaDpauM39+Oz6/zjVX5RzuZgohxBFNujeEEEIcUqvXxHjyxXZKYgnK2jspiMXJTVoEHZcMyyY9keS1N9pRSh3upgohxBFNgrsQQohDQ4HdbrH87RYy4xbhRBJDKVy9767HBFTSZkeVdXjaKYQ4rNR+/BzrpFRGCCHEQfX6vE5WzJuJ0Q53/nkRQ3c1oM6ZjieRAKVSP1rfi9H8joupy25ZCCHejwR3IYQQB81rb7Tz37srCbkulmmwfnQZy8cPx+e6OJqGqRSG6+IYRvc8muviGjrhwL5PAivHxX5pI25tJ21jS9iih/DmBZk23ETTZEQKIcSxQYK7EEKIg2bp79eje0NYWmr3kh2LkzQMOjSNiMckPWnhsR0MV+HoGrpSmLbDxvQwL1+7BCM/QM5ZxYyamk5JRRCAul8tpeanC/HYFh4zhqZrDE0mWVBWxi+uOJe/3VxMdlgqP4U4esnB92BJcBdCCHFQJJoTxKIKfL12wppGKJHESDg4GQE8ySQe103Vqmqp4K6U4uRtVQxbuorMSIzGR8I8MH0iJ3x5KNMTLdTcvJDq3CBbS3K5ZOXy7kWfUllJx2Ov8+MRF3PPhSaJHW0EpxWi6RLihRAfTxLchRBC7Jdoq0XTwjrSNBfPtHwCOT4Ati5sGvDiMc11ac1OwzZ10mJxvFbfi1AThsFFC5aR8HuxdZ3c9k4uXLicZzSD8vUr8PjjbCsfzZjqqn7LPnPHTmr/+BrN176L13XZlZFG8PEryT1n2CHYciGEOLwkuAshhPhA9Tsi/Ot/1hOrjaK7LmgaCkh4q9Ash7b0EI6jKG1poy0rrc+8StcoaO9keWEewWCAQDI1ugx0jRLhuDx7xvEkPR68lsXM1ZsYVttAQX0LgfpGnpw8mf9UDONMZTGhqrLPsqM+P25mMXNP/iQnb1nDmJpd7P7CozSu+g5jSr0fzYsjhPhQlJTKDJoEdyGEOIpYUZvNL1TTvrODjk6XkGNTdHI+w84p/lAXacbaLHavbsWwHCLrm9HKMmHTbtrW1BDM8fLCshA59e0kMsPYhkFzepikbpDZ2k5JbROF1Q1orkIH1PbdNOVkYCpFuCNKa2aYUHuC7Kw0tqWHiRgGZZ0RdNfFTCTRTINkV3lL0uNhwXFjKGxqYVdZHtur0vjZCdNJGAYrMtM4eft2xjU2AuBqGu+NGJVqv8/Pa+OmUdDWQklrM1/8RS0v/answ77c4gO0xhVbWhWNUUWGT2N0tkaaFzyGBDEhDgVN7ecdL+69917mzJnDLbfcwsUXX9zv+RtvvJGlS5fy4IMPMmbMGBYtWsTrr7/Ohg0b2Lx5M5Zlcf/99zN9+vR9rqOxsZG//vWvvPPOOzQ0NBAKhRgxYgRf//rXmTJlyv5vpRBiUJTj4rbEMXKDh7spYgCd1VEe/uw8aNvFspwRbMrJZ3N+Gpcv38bxHa2c9c/TyJmWu9/L/ff177JufRyta8QXlMLyebuHbEwaOtmRZpRrkjB97CjMI+nxdM/vjyWYuG4LhuVgALZp0FScg9s1coyjaawszKMqvasnXinGNTYzZfMOOr1+IrkZfYaHzGiPcNKatbR5wjwxrpSHJ0/ofs5n21y8cRNn7NhJW0Ex2/PziHk8hBNJciJRTt+wktLmen5y8RdpCgVJopHpVVx/eRYe2+W915uwK9spLvZy4qXFdPg9jC73E/JJ0Nxfk/+cYGW1Cz4jdW2hrUCBR4fffMLgOzOlb1AMjqVdO+hpPerPh7AlR779/l913XXXMX/+fO644w5mzpxJYWFh93NPPPEEixcv5tprr2XMmDEAvPjii7z44otUVFQwfPhwNm3a9L7L37x5MzfccAMej4cLLriAkpISotEomzdvpr6+fn+bK8QRI9mcwAyZ6D6DzlaLF+/ZyeZ6Rd6IEJP9MdruW4dHU5RePYJhN41F0z+6ING8pZPnvryQtphDNOAnEfRQ5LXJLQ9x4g0jKRyf8ZG1RfSl6tqwFlSiOqP88S+dpLVUMX67xtSNm2nz7WTx0AJuP3MK4/+zgHmffIVwiYdRJ2ZReG4hgc+O6zdeupN0qXpqB61v1ZE+LoPqe1azekgpnj2hHbA9Zp/5vI5LzAjTnhbAYzvYhsGwqgb88SSVJblEAz7WjKlg/PrN6LaiMyvcHdoBDKWoaGljd1oYpWlkRWNM2FGFpuuk2UkCDc0052Th6honrNjE0JoGANJpJD+S2af9CdPknbIyrlm9jjkzSon4U/X1LaEgnT4voUSMF4+bQX4sQVMwiBdFZnOUh+5P4LVsSltb0YDtNTG2LGun0+uhMhhkW3qAc/UYnuoIbYZJdVoIFTC4aLzJF64pIhA2ONatqXV4bLXFW1WwpBpiMRcCBugaWG6q7qnr1+++YvPObkXYUJSEoCxb5/LxBun7eYDkuIqmqOLfG12W17p8dbKB7UK2X2NknlyELI49+93jDrBhwwZmzZrFlClTuO+++9A0jaqqKi6//HLKysp48MEHMc3UMUF9fT2ZmZl4vV7mzp3LXXfdtc8e92QyyWWXXYbH4+HPf/4z6enpH34LhTgMrN3t1Dy+g2h1krb3GmlZUI9jgeXzYJs6huMSiFs4ukZNcRbFu5vZc++ZmN/Du6eOwgqYFJWYzBwXJEyE8jPz8Vfk4vEMfmelHBenOY6Z17cH/cm/VPPma+04LuQ0NTNkVxPxdF/PfICr6ZhJGzTFaes246R7WVFUjBX2M/MbI5j85fL3XXfdxlY8C9fjH11A8MT3n/ZY5NouVksSX56fxOvr2fl8NU11GiG7g6FnZrPeyqP678sprtpCXks7G/3jiBgB0pJRsjqTzJ0xhj+fMIGkaRBI2ly8disXbthJNM2L7rh4LAcNCGkwZEqI7JEaLy8yiFkmRZUthNvjJAM6nRkBtpcXEY7Gu9tmeUxU75FZlMKbtKioqmFNeSnTVu4kr7kDANvQefG0SUxZvZ13Jw5l6O5aWvKzcbyePtvb6fOyuKSQiMfDGRu3MqSlve/zoSDpsQSnvre+z+OWofjX1NFUp6exrLQED4pPV9dj+X1sz8nu+6Iqxcj6BmyvD00pGvw+IqZJUVs7PsfB0TTSbJuE14tlGKnSIqWImia2rtFkGGxNC+GYJhl7DmI0DdeAojSojGiYSYcS12JdUQYtWUG+e7xGvm2zfIvF1Wf5mTbs0NfVRxKKBxYn2d7s8tUZHjRDwzA0xg8QZP+5wmJTvU1WUGdFnSJuuZwxzOAr0720JRRrGhR+zeXeJS75YY3/Odng6Q0uu9sUl000iLkaBQHFa9scvvq0BV4DTD11Ey27V2+7AgwtdcDnqlR6dxUEevoHNcflvrN1rp9mUtvusr7R5aRSnYSjsaHBISugcdl/XFbUp27SFdIcOmJdRwNeHQy959aZriLsWpw13o/lwrlDoDPmMG+HS20ERubrnDHcRDdgZKbGJ4drxG1YWetSGNYYltX/tWqJKd6pctnUrIjHFWNyNS4YbeA15WzMoWZp1w16Wo964BC25Mh3QMEd4IEHHuCBBx7gBz/4AZdeeilf//rXWbNmDXPnzmXEiBEDzvNBwf2///0vP/vZz7j99ts544wzsCwLx3Hw+/0H0sSjW3UzLNkMk4bB8IL9n7+yAe57ETwG/OAzEPbDyyvg9dUwogiuPB2Cvr7zLN8GVU1wxgRIC8DCjfDeVpg5EmaMhOYOeHs9lBdAaxQ27YbOeGp50ytg7S54YRks3QqfnASVTbB8OxRmwE0XwPB8iMRhfFfd6dx58NtnoCQb7rsOnloEO+qhIBPe3QL1bal2nD0JirPgkfkwohB++gV4ZwPUtEBJFjz4JnxiItxwHuxqhDufTbWhIBO+cga0dAIafO6E1DYv3QK3PwPjy3CmjUH97jn09nY014LWThyvH/fTM/BmmPD6Gjh5NHxqCqyuhCnDcScNp+P38wjMfw+vT9F5xvHsemgXdoeFHjRp2xDFUh6yVBNJTBq1otTOTCmGaZupV0VEtb4X7+1t06gi1k0s69PrqbsOXjuB3zQJ6y6BsM7x8WqmUoUnEaVhVQfr0orZcNwkzmnaCW9WorXF0QENF58RIai1sbmgkOcmn0FjOAfTsihqaCYci2GZBl7bAXr2jRoQbopSXZhDyElS1NRMfVaq910PGow5JYe47bBtYQNKuXxCbaSuaAzRBbsZG1/HruwihjVX0R7KoDpWhu162DptBEMvLmPy+lVseKURLZlAz81m+mX5ZKRZaCePQo8noaaFDXoBTasamRDZQVp7K51OJpbrwz+zkMa4lwbXS2aWQW5TG97R2QRyXLSuz/B72xO889JuSgv8VAS91KxupX59O511cXy6w8SsFiZ/voTdw0bhC3spmpQJQDSp+O9LTSTmrqUhCStPGckSO0RrTYzjt+/k/6xeTvkQH7FpJdytFfFfK5sJVZWMbEtw3WQPpXor1c/sxrY1cm6YjPGZMaxcGcVojzP51EwSL+/kvT9sxqmJYzqKSEhHAY7XwKMsJrWtYlH5dJpCOQB4YxYlO5v7XLpVG/Zz/aVn9vnM5ERi/OaVhT0fGaVQXX+MatvC1pKhtATzUs+5LsH2KKrrrI7lMUkE/Zi2i6YUsZA/1WPuut2lM7qryO9s4NS1S2myh/dZd2tagFBnnDUjizGcJK05GSTCfQ8WG8IhqjLSqAoFuWDlejLiiT7Px31eSutbGLdtd7//D149wTsTx9KQmfrsuUBzWohtef3LgkraOvB2tRnbIpy02NNXbmkaS/Nz2R0OErAdRrd34gNqQ0G2+rzs7nWwEXZdhls2mlI4ymW7z9f9/9FUihzbpiojQCToo6wjypT6NhSwITvEqsw00HU0PdVWj0cj3VCUZulcOgLmb7NZUK9BwiEQNjA0HY8JWX6NM4dqlPhdVlRaBEyNy6Z4Wdmk8f/ejLO73cbVzFQtiqMgbqf+s+oa6BqarlGerTE2R2Ndnc225q7Qq0H3ONka4NFSoVuR2kd4jJ7vGseFmJ0KykFPTxCPJcFjpoL7HkqB5aSCvHevMxJO13P+vU7sxyxoT/S9d72mgc9MTat1LVfXwdTo88F39nqzlYJk1/pNDTrt1PRBM3UQYavU9mhd7dlrftMHaX6D9ICiut7BUqS22+2awFXk+eBrM01eqdRY25Ra1bQCWNMEnUkIe8A0oCgEX52o0RiDZXWKLF/qZMT2dqiLQoYPhoThqvE65w4fuPNFKcX8Kog7ijNLtWPqOgEJ7oN3wMHdtm1mzZrFjh07uPjii3n00Ue58cYbufrqq/c5zwcF95tvvpmXX36Zv/zlL/zpT39i6dKlKKUYPnw41157Leeee+6BNPXoc/9LcNNfwHZSX163XQY/vmTw8z+zGD7/29SXLaR6KaYOh3e39kwT9sO8n8O0CnAcuOwOeGJh6rmMIJw7CR5f2DP9509MhfJo351tt65gOihTy1M7isWbB79Ng5EWSLXPcQd+PuBNhfBXV3c/1LVb66d3cO2tmRxWmDNJKi+6chihNlCiKlmuz6RVy0ZXXTvWffCpGAkt0P13qsc9G9tjULi7GX/S7n5u+dTh7Czfx0GbUhi2g+Mxufadh5lYtYl6vYyoJwM9YZC6RLC/SBA2lhXSEMqjOjeb5oxUbbHuuAytqSWQSPaZ3oXuJSVNk/rcdCbs3EKHJx1HN4l5PBS2tlFe20AoHsMyTVrCYRJpNi9MOaV7OWdveItGbzYrho/vDgjBeIz/++ofKIg0YWsGunLRUX3ek7jhJW74yEx2dC9rJxNZXzicTUOKux8b0tDEOTtfJ40mAH554mXYWh6ZSYv6gI812Zmctbuu3+thOha2kQprxVMyOeHnU/jht9fxw8ffwOO4PD1uBD8552QcXUdTLpeve4//nbeIN0dOpC0QAqUobd/BxZtfxtF1XiydRF51BgV2O35loYCYofPC+GlgGBhJm7S2aOot1CAW8GH5TTSl0FzF9NrlJPw+3iucjCduoSsXX9whuznWp90vjirl/pMnMGl3I1e8t5GcaJxFQwsZ3dpMViKJ0jQ8SRfTcnEMjYacEFqoqwPEVXgSSXTbSa236/MU6rQJxlLvv2Xq1AzJJhnq1WmiFLrjMmPtBkKtfV9HBWwryiThM1EaJL0emnIzcb2pkpuo10tbOAiaRkLXKKxuoKAzgm3oNKen4Rg6Pscmv7WTk5dt7LNsHZeqkmxWjOh7sGDrOqtKi7F7leSUNdXzxXcX8MyUE7G9foxkknCvoSfnF+azLSN10Gy4LhNb2ymNxXE0jSUZaWxIC/dZR3nSIqQU27wmmqtwe52FaMoNkfT0rDsnluTCbbVYmsbjI0tIGl3TaqrrM5+q/8Zy0AIelK5huC7j6tpp1TR25ab1DbORZOoHenq003wQ6HUmw3ahI9m3HErXUt+Be77/6Vr/nmk8emqaPb3iAU8qjfaWtHtCu+NCsutAyNBSAb33+iwn1b69A7rbFZo9ewX6mAVte+1HvAak+1PTR5I9r4PfhJCnZ3l7vtr1rtfEJXUQ4JLq+ffqqTY6XdNbTmobIbWNe9qtkToTsCcY213TJN1UwN+bBoT6lo99GJ8og/9+zsDXqye/Na4453GHpV1fU8PS4bUvGJRnHhvhXYL74B1wgZhpmsyePRvHcXj00UcZP348V1111YdqzI4dOwD4wQ9+gMfj4Re/+AU/+clPcF2Xm2++meeee+5DLf+o0NIJ352TCu0Args//Rfs3I/6/hsf6PWlTerLsHdoh1RP+bf/lvr9mSU9oR2gLdo3tAM8uXDfoR0GH9oBlm07+KEdoCO279AOqR6jXqEd9n2vNm2A5xSwWp9KUqVOhbuawSZ9PEm8THTfA6XeN7QDJLQAHpUKYUmPwbyzJ7L0hJGsmFZO9ZC+p/070wMDLaKrgRqOxwSleGvETLwkCLudBBOJfYZ2R9NYNHIcdeECOgMBmjMzu3dErqHTmNG/jr33kry2zadWzeeq1Y9xzcq5DG3dSXFTMyet30xhSytpsQTZHREqauoYs6mJIfWN3fO+NuoUducV9tnxRf0B3hp5AgCmctC79ta9X3e/k+wT2hME6DQy2FJcSG9VeTnEu16uXeEcvE4WmclUYMuPJZjW0Eytf68zTNAd2gGql7cy545tzHrlPTyOS9Q0+d8zjsfpCmtK03lk/AyeGzsuFdoBNI1dGcPZlD0Cj+swtaqebCeCX1nd2xJ0XE7cnfrchTrjKF3DNXSUrqNpCr0rPCtdY3HpDFbkHkeoNYI/lsAbt1C4tAdNXhhdxtxpo1iXn0V5cytDWjv56cvvMqqpnZxYkvM3VFLWEMFwwZN0sD06kXQviYBJXEuFJ91xMC0bdB3X68HtKmkMxHpCO4DHdslqjvR9sTQNpWtU5+b0ex0tQyfhM3EMnc7MEImwn3A8jj8aozYjjba0UPd773MVHRlp1Gems3FYKdUFudTlZlOZn8fOolxacrzQ/Vlw8ZKkNRzqt07ddRnW3EJaPAGuixnt4FuvPsfYml1csGIhrT4vzR4P69LCLMnKoNk0afZ5mVrXyOSGJia1tjM0ljoj5VGKk1vbKdrrLICtQULTqPb7sHtdc5LwGn1CO0BTwEuj34tHKfL3lB1p9PrMd/3rN7vPdDi6zurCDCa0RfFbe3UHBz2pgA09PQx7h2NT7x+MXdU3tO+haan5fWZqHp+ZCq8DXUtj6D097VE7FWwdlQq2yb2+Y/fVaeO4/Q8IYOBgvOdgJGr1PXiJ25Bw+s5jkGqzpqWCd6jXgYyhp0K7TuoAJWCmAv2edu7hM3pCO/S0c6C2QapN8b27+w/ca5Vw34q+67prmeoO7QA72uGWd95nf/Yxo9AG/XOs+1BXdoTDYbzeVIg56aSTMAzjA+Z4f9FoqieqtLSUu+66i09+8pN85jOf4a9//SvhcJh7770X1z0yPsjNzc0kEj1f8p2dnXR09ASMZDJJU1NTn3lqamre9+/a2lrUht2pgNmb68KqnYNfR23r4DZi+Xaam5ux39v6wdMK4gSIaf0DRIuWQ4gIer/zuAPLVK3oymZHeQG59e2c+fJKznxlFfl1bX2my2rqHNTyLD21M/diYdE/nHa3My3UPRJIdIAQ63Xsfo/trSZUBEDASXDOjjcoaWoZcDodOG7rzu6/la7TGuh/zUp9eP9GQEkQIu719On53KPNl1r+2twReN2+O8WMpEXnXjXXA7E2NBPsCvxVGWE6ff1fpxUFJf0eqw6nDiQiWpiwG+/3fGF7M5qrMBy3u3wFwO4Vulw9FZZMy0bfKwi1h7386cRxPHlcBTeffwKxsMUP33iXvb9xPY5CaRBN8+N0hRGla+TFYqikjW67fXZ7ytBTvfN7h0bAF0/2ewzA5+kkT9uJ27Wg1vQgm8vzU2O6B7x9auO9tsOZ763h2n+/wvHreg7WvZbLtpJCLNNAd13iukazz8fmnGw2jMyhQltBBavIphEDRU57B3tzDB2/7UA8zuvhAL7mBrKiqYONidW7iGiKR0qL+FdpMc8VFXDPiOGMbmxmclMLUxuaKYtE+y1zWKznzIauFEFXYWkaBZZNW69RdOL+fXyWul6Tjj2ftb0zhqalwuVej7UEvRREEv2n7R2qe/eaD7DO96V1lZzsHaQNPbV/2Zvt9pTB7K33Y0r1lOEkez3uKohYfTuQ9thX54pS+55eJ9Wr3udAqIu3q8beb6TmN3q9TprWU8LT+//UQCUoH9T3ZB9QccI+vVlp9dmfL6vt/1q/u7vvjcoGzAy9tutAcok4+hxwcFdKcdttt5FIJCgvL+fBBx9k+/btH6oxvq6d5IUXXthnPOLMzExOP/10GhoaqKys3NfsH6ns7Ozu9kLqICYtradu2ev1kpPTt2eqqKjoff8uLCxEm1gG6XsNxecxYcaIwa9j0rDBbcTJY8jOzsY8dVz/5/YORh/hCCdHKh9xvKp/KEtXbcTx4/aLUQPr0DI4w32JjGQbk1bsIKM9RkZblPBeO+5RG3aT2dQ/sPShaRxXnbqYz8bEYN/h22f1POdLWv2ed7T+Xwd776pyoq3dvwftOKbqv5yBmI6FZfa/aG9c3QefeendBj8dhOOxfu3XXZfiztRObVLDtn7LsDWNokj0A88MFUzKpi4zdQAwrLWdnGjfXmfddZlat6vffHmx1M4w160npvUP+wmPjtJSpVH73Lg933kDtNF0ewL31OoGitsgf++gRyq/WD4vut0/BOwM+AcuC9M0kt7+Z4psc6/Ps1IEk1FO2/kOq0cO562TxzL/xNEsmzKcuuIcYkE/7t6hFEh4PQSSFies3czYHak7nw7fWY/PtgklLWxNozEQIOox6fR6eXr8iTx44oUEiZDPToK0MbS2nqKm5p62KcUfRw7n7oqh/LOsmLiuc8Xald3PR7xelmekE+3VmeToGi8UF9KUFsbRtH4HR5AaFx6l8DsOZZaNh1St+8hYgpI9ZWRKDZifdVeRG0uyMTNEm29fB4kDh9PMuEXHQD3nttv376TTfxpr7x7wfax6X49bTt8wbbupZe6pnx9gE3Dcnhr5PQcYSTdVttKegJZ4qvc6ttf3kdO13L3t2a6B9jN7Slw0Bk4se95HXRt4G/c85qieaQfqWf+gC1AP8i5wWpGnz/78lCH99x+nDe37ORowM/T6MB5ILhFHnwMO7o8//jhLlizha1/7Gr/97W/RNK27dOZAFRSk6nlzc/v3wu15rK2trd9zHyvhAPztxlSdOaQuprzvWijMGvwynv6/UNCr7CE7LXVBZ++d89A8+MPXUr+fNxVu+nRPWB+aB/d8FUpze/7+/dWpiz2hf4+HZz9HFS3Jhi+d2vex/akd3NdOsTQXZgx8YTSQquu/eMagVtHvU+z3oKMYl7UNvdf3a6m7jXRa2a5VEKQTQw3cS9lb3O/hP8edQ6Cp//8Vu1fwMS0Hj2UzbfFmzly2iLHrt6Pt1TuWFuvg3A1vYeGjiSL8xOkpBIXee97cWAPlzTsACEdjpHf07dFvTQuR6PVeKvrut0PxOCfV9JRQJQwvW/PzsQfo/XY1WFWeughZc92ekpQ9O06lGLNzK6dsWZyaHnC7vo56r7fVl87bQ06g1ZeOq2lYBDGUzZSt2wl2lTV4LYtJ2ypJGrkoXaco0kC23be0rMnnJeSk7vaZ31lHcUc102qWkRPt6X0aeXYB1/5kBKs+NZ5thblowC9fmUdWPBXe/XaSO958lHG1O/BZPe9zUUc1YxtTddlBlcTVbZxee3mDCO8Vj0nVd6f5+gRzT69rGrSuxx2P2S8vLSkpQGkauuty1YoNeF2XhNfoN12q19szYIAOWzEa/d5+08dCfpoKUvXpezgaJEwwYwm8SYtALEFuSyunLltKQ7SMLVkjcA0dq9d3SjQjiDZAb2pOe8/nbMSuGiq21EAiFdo1oHmvsz9K09iYNpTNTKSOIeSyi9VDiqnPyMAGdublcP677/KD+QuYUl3N2Vu3869nHuOE6qruZTw9+XiaPP0PFNtMg4TXQ3soQCCe7PNeRHWdJtPk69urOKW1nbS9gn1ZIsklu2o4u76J8AAHRiHL5oVh+SwszoG41T8QQ1cwVd3vNcDohg7adJ3mYK/PhuNCWxxTh/TeL09bLFUj7rg906Z5UmUhe+r7Brpwx+0qddn7oMFVXReRupCwU/Xle4J13EmVqez9KTO01PRJp2d5e8pWNK3vgUTCgdZ4qgSmM9k/yO9huake+r3PCHiN1La9H1elXlelBt6PdFcNdZ0d2NP+3q/FnnkDRk8qMrS+CekD2hHywFml79/UPaYXwP+Z2ret35yicUG51mea/z1FhrsU/R3Q3RGqqqq45557GDt2LLNmzcI0Ta6//nruuusu5s6dy6xZsw6oMRMmTGDBggXU1fW/iKy2thbg2Dha/PyJqVFM1u2CUcWQ0b88430NzYfaOalRYuJWalQYXYebPw9rKlNfcpOG9e1Vv/tr8MPPQW1L6jnDgK+fmxrZpSAzNe0Nn4RVO6EsN1Uj3xmH/AzICsPmathcA9lheGsdHD8ShuVDXQtEk3D6uFSdfWccPnFcKnz/8gp49B0YXQwXHQ87G2BrLeSEU8tKC6RGx3lkPgR8MOtMuOTEVFueWAh1ranRa+a+BWdOgDMnpralvjVVw/nU4tRIOC5QmgOXnZKad1M1PPYOjCxKjXqzqzF1cLKlFrczTmd2EercKWSMDKXWf+o4KMyEulYK8zPIbrFoebue4I6tpO124bqvMzYnzOi7nmf3C03s2ukh3pLEdnRc+l/Q5CY8sMOH5ev/36+uIANH1zFth+bcdMavqSItEuGM+CLQkixum8lr46bSnBYmJwSfatlJ1bkX0X72BCJPbsXfFCGYrxNoieEkgMlFBEdk0v7EJjrag+R1OqwvMukIBFGahplMYneVu6HrbC0tIRyN4bFtNMdl2qat+Cyb9lCAPFVJWle9uavrVJ5yBsPHF7P71ELSF+8g1NZJMOwhkRNmU14WJ6xfQtnKnezMLWVd0RiiWoihsSh5JxSRPzZMWmcZG3Ouwr+7Gq0in8KJHtzdEThxNOHTi3GbO6jZGCe0po7wpy9CP3UMoXiSEa+vxlEGo4NZbNvp4MnyUj7+ZAJlX0VrbIOaFq6cPIxdS5rY+PxO0ovTqPjUEKINCaItCfxpUyiu3IJnzk5OLm2m8Wun4SnNIqMkdbD8sweOY8uSUmrWtZGfG+Zp1U7s6QVURJpp+c5J/Li+DHtnI1duX8cplbtw9CDbp5yN77RyCr9zPOV1bTiJJPHfv4EeiWGfcganfXoitqmTNyKN7c/s5L1frcaO2ARsF0/CIqnp2IaL0nVc08AKeAlFYuBCp+nhkQkjAUhLWmR1lbC4pk7Cp+NLuN15LRo0MS2HZNCHvyPWnd9cXSdb97A2K4NAQzMh28HVNGyPierqWa8emkugI4Y3nsQ2dSy/F8djEvd50ZSLa2s0W0MA8MUsknuVi7i6xpZhQ8htayccjaEpRUljC2X1PQdHmQ3tZPk7WDZuaHcmcgcIW6FoggjpREiniXyydiVJz4iyKz+ThTlZ/PdrX8Fr21yycg03LlgEKNblD6UqO4NXR4yiLj2LsOvSuNcBTGlXr3nC4yGrM0q7grihE9N1HAXn1DVh69qAPVqagg6vyYqiLOoGuP6kw+ehw+fBoymum2lSXZ9k0W5QXpPibIOCNB2/rmhttenQDJyoTVpzjEkjNK67IJ+FdRrr6hx026ayweHUci/fPMVPW1zx29fjPLYiSVsSmhwb2rtGT/F2jcJiperAg144dbhOxDZYX+vQFO1Vf+66EE3iD3sI+XWU69LW6eIYe0K/lup1tnoCrYZC7SlT2RP8426qVt40Uus1ui523TOb1+gptdkT5C23K/iq7ul0QyM9qGP6TZptcBNdvfh7Lhz1GD1BXql+xw/dbAVpXjDAo8Byex25KJXa7oBBRkAjkUztEgHMpMNJZTqOrrGqVmHjEDOMVALfM9SllSq9yU/X+Nw4vfskw/nlGi9tVzy+KbXqm6ZqfHuajqFrNMcUi2oUdRHF8AzF6kaN3R3wxTFQG9EIejROG0K/uxwHPBrPfs5gS4sibsOEvGPtLPextr0Hbr9HlXFdl+uuu45169Yxd+5cKioquh//2te+xoYNG3jooYcoL+8/bvMHjSpTWVnJpZdeSkVFBQ8++CCerprC2tpaLrnkEgoKCnjyyScPZDuF+MglmxNYzQm2/2o5tf/cjrJcjPwgRthLbGsnEb+HQMLqHr/d1TTePn0cLblhJnU0EGxKUHJiNqf/YgJGZxwyA2gDhP0DUVcVp+r+Nby22mGbPxMAw3HIautAQxGMJwh19WgrQDN10pvbyI91Mv0z6eTffMYHnwWyHWhsJxEK0borRnZ5GI9fbmID4FouiZYkgfy+Q90qV7H+X9tpfnYr+aVBkscVsvrejbxDmMcnVlAf9PPbVxZREOk1yoxSmJZLvKu3sDUUQvN7UjX1lo3SNMpmZFGTGebd8iKylcOUF9ZTU2+jDB3ddrpr3aFn96lUqgZfGQZKKaJ4OPuNtQB0pPvZOLEYd0+wchUdfh+1uakLrHXHoai5lS++vhCz6yxRp8/LE2eeSDQY6FmBptHk99EQ7AnCmlJ87fm3qajpubi5HT8ZRLn7nON5dUzfs2o/f+FlztqylXVlQ1g6YjR/G1NBWSxO2LJZlJlBs67haBpFiSTntrQRdl08lk0gkWRHbhaWYRBKJPFbFhHTpCEYoCASZXtGWp/rEY7LtMnJ86DyfIRHhAn7dOojLsMydSJJxSvbFWcM1fjqpP2/ydD+aIsrNrcoxuWmQmDCVjyxyqI4XeOMCrNPIKxpd9nc6NIUcUnzaZxabvYZyWTP8pK2wkWjIKyxqsbmttcsxhdo/PhMH15TY3mNw9kPxmlO6qkw7apUqFWqb0+3R0sF+j215o6b6rnvOo1mGHDr2R5+fNrAY93XRRQdScXvF7s8s1VRH03lbt12yA0oGl2z70kDJ7We88s17jxLpzxD46WtLk9tdKmKwaQ8OLHEYGqBRmn6B78nScdlzkqXDB98foyB1TUyZk5AQuWhltSuH/S0XnX/IWzJkW+/g/ue8H3TTTfxla98pc9zO3fu5Etf+hIVFRXMmTMHwzDYvHkzb775JgCrVq1iwYIFXHTRRRQXp4ZyO//88/vUbf3pT3/iz3/+M+PGjeO8884jFovx+OOP09LSwt13383xxx//YbdZiMOuc30rNY/uINmcoHlXjLijET91CBmTc5g8I0x65kd7q/DqlS288Zv1dGxsoqqwEFfTSY/FyM83uei28eSOfP9x58Wh1bq1A83QuPx5hy2Lm7lx8Wqy4kksXSPuNTGN1Eg1r5cU8tlPpKOUjm9XO8UTMjjhmuHoA4zu0bmtg6pnK3nt/p08O6IUj6EYnkxQEEtQXN1C2Y5GLJ/JzrJcagoyqCrI45xXV5HfkLpxkuUx2D0sEy3s0GEE2F5USPteo78UNjRz7sKVKAULjxtFbXFen+dVV+dok99Hs9+H4bhc+tYyjtvedyz3dvxk085Xv/JZlGHSEPB1X6B88Zq1/PiN19hQPIT/TJrK8+VllLd3MrmxmZeHDiEGTGjrINe2u1dqOA6+pI03mqQxI0wy6EEf4id3SIi6+iT1HTZBBRnZXtIyTM4+PsD5J+117dExJmErXt/mErNcltSB5SjuWKRSpyKcrh713uPBK4UnZvHgxV6mFmkkXI2JBVq/nub9kXQUrXHIDsBzWxWtCThvuEZBSIL10U6C++DtV3Dfvn07V155JaNGjeIvf/nLgKPIPPTQQ9x5553ccMMNXHPNNTz77LPceuut+1zmQL3v//nPf3j00UfZsWMHpmkyadIkrrvuOiZMmLAfmyaEEB8vnQnF1+9p4NXNFmMaWzmhpp5hnREipoE7rYBv/X4CvvT9u3Ona7vs+sc21v6rkvXpaczzhCnpjDO0tYOMjihVxbk4hkG734+raYzcXEN2S4TG3DROrX2Pqbs3Y+kG/554Km+Mm9pn2QX1LUxZvQ3Dslk2dSQdGX3HSVeOy+6MNDpMg4SZGt70+0+9xpCmnmuZbHS25+ezeHIp2YkkBhAzDF4aWkJ1OMTpO3dy+3/+zVNTZ/Lw5Il0er2cXl1L2LKpzwpTGwwRCupcNNNHWb7BxOlpqITN5hUdZBT4KJ+YhnEM3ejmYHpzh805/3KwVM/rV5GpsGw4u0zjtjNNStKlTlt8sIT2jUFP61N/PIQtOfId8A2YhBBCHB7JiE1rZYTGUICNVUlOHucn/yCepYltbSfSZuMbFqa5Jk7djjj/+XMVDb4gjmGAUoQTCS5e8TYnbV8FQNz0cNeZl1KZW4imFOnRGIGEhS+RZPL6HawaMYSGgr2uUXJcWoN+bF2nOhjA1TRC8QSfWL6Bsbsb0F1FVX4mDQWZaHuNONLm9fDkyOGMjCX45No1vDpiFAHXobQjgqerfOPrvxvF0LF9DxbEwbe1xaUmAieVaOgH6SZF4tgiwX3wJLgLIYT4QJVrO3jjoWqqqxJ4WqNoHUny65s5fdNyglonHb4gK4eNZZs/n85wiGivCzh1x6XG66UgGiMeTpWcGJZN0mOiA0nDIGnoxEwDj6uIGDqlHRFO2LaS9txCWhMm7t7DUwKLhg7BMU0y4wkSHhOPbWMpCDgOZ0z0MOvH/a+1EkIceSS4D95HW0grhBDiqFQ2Po2v/Gp0v8ejrefj1EYJenUmbetgx4/WEE3re9Gta+jEgn4W5edQ0BEhqFxUwI/uuoSSFl7HwdG11DCLSpHbdXOk8bEOgsNDPL0uvd99EhKGgWMYmI5DwLKIeUw6Qn5GF2h87XNpjJrc/4ZfQghxtJPgLoQQ4oAFMz2QmbpvRGlxkED0XTSl+ozIAuDoOn7HIaBrqK4Q7uo6UY9JWtJCU4pO08QG2sIGV25Yxph3voJR3cjXTvgt/xl9BtWZqXt9OJrG1rwc/LbN0OZWajPS+On38pg4cT+HzhVCiKOMBHchhBAHhek3OOHakTQ/Vkdbbs9woXHDoMnvI93ufwMeR9dxgCFFMHKMFysjwMVnBEkv6hr2Ma+M9H9fzxd+9h+a1i9jafYoVheNYHhLKyioSw9z0klpEtqFEMcEqXEXQghxUC35/mJeWJxAN3RcXWfRsCHEPB5Ctk1e102QvEmLaRu2k9/cRlNmGnbM5rp1F6D7PnisfyvuULkpQkMHDCv3U1i0fyPpCCGOLFLjPnjS4y6EEOKgmvKrqaz71j+oWjmMmBGkKBKlMj2NuK5jA6ZSXPL6EoqaWgEYsbueTq+XxPYOAmMyP3D5Hr9BxXHpVBzSrRBCfFSU3Dl10GSAVSGEEAddcrJD/le28vPXppPhhSGRKAHHIaFrDK1p7A7te4STSRL1kcPTWCGEOEpIcBdCCHFITbu0CAcY2t5JZjxJbmPLgNN5Ax9cJiOEEMcyCe5CCCEOqS+cl85xn8ihMiPMrvQwr04ZQ9Lsu/tR2X6CMwoPUwuFEIeXth8/xzYJ7kIIIQ4pr0fjB1/N5pG7S7jsnCC5uV7+ef7JtIYC2KaBOTmfsWuvONzNFEKII55cnCqEEOIjkRYymPXFbGZ9EaAEmHKYWySEEEcXCe5CCCGEEOKwkVFlBk9KZYQQQgghhDgKSHAXQgghhBDiKCDBXQghhBBCiKOA1LgLIYQQQojDSGrcB0uCuxBCCNFL1dYo98xtwFm+hbhXMear0/nmpzIPd7OEEEKCuxBCCLHHHx9qpObhLXxtyeOMbK4GoOW5f/LXO77HV78x7jC3TghxrJMadyGEEALY3uyw4pla8pu3dId2gKx4lGG/fZyrHo3huuowtlCIjyeFNuifY50EdyGEEMe8jQ0uI++IsjMUwJ9o6/f8uIYa5q52OevP0cPQOiGESJHgLoQQ4ph3wT9iOErjpSEFzBs6GoD1OUU8Mv54tmbm8caw0aDBm5WKeNI9zK0VQhyrpMZdCCHEMW9LiwItdRr+kQkzaQqEeXHERAA0pQgn4qAATaMjCX7vYWysEOKYJT3uQgghjmmPrLBSobyL0vTu0J76W6PDH+j6Q5EVkDpbIcThIT3uQgghjll/WJjggb9updSXRmYiTn0oTF0oDPo++rU0DdOQ4C6EODykx10I8bG3pdlldYPUJYv+/vfhOgwXdmVmsbqwmLq09FTJjNr36DHPrk/SHpfPkxDioyc97kKIj62lNQ4zH1bsiVgB3WXV1TojsvbdZ9EcU9y/wuH+VRAw4fpJMDFXozxTozxT+jo+LpZX28y4L46bnkltZk7fJzUNXLfn971c9LAFWADkh+DJy32cMkx2p0IcKBnmcfDkm0YI8bEUTbrMeLhvr2nMhZlzXZr+TyqAv13l8udVLkkHvDr8cyNYe3WkfncepAqgFZkel+wAVHZAlh/+8WmdTw1PLWt1g8Mj62FkFlw2RifokR3RR2LFdpyYTXJYCYGiYJ+nHl9jccMzCdoTiuHZBm9f5yM3ZFDdZHPK3REc00TbKzB4HRefq+jUNZSrUueldY2p1du489WHmVhfxdulo7jpk19mR2Y+9RE49S8JFl8Pxw+RXaoQ4tCSbxkhxFGvNe7yy0UuSRd+cYpB2Ksx8e8DlzI0J8FyFDe/7XL7u/t3M51WK/UD0BCD8550KQq6RC1os3qm+8YrDiu/ojMmR3roD5naFirP+H8sSIwmZoTxJleTo8c46aXzSR+dyX83JLnskQSurgMaG5sUQ37ewdTGDnxKMdLnYX1akKRpdC+yLJKgMGGhAZYGm8MBOrpq3Ye1N3Hqrk0AXLBlBUPbGjnuul91z/vlx5Ns/I7sUoUQh9Z+71Xuvfdepk+fzr///e8Bn7/xxhuZOXMmGzZsAGDRokX88pe/5KqrruLEE09k+vTpLF26dJ/Lr6qq4ic/+QnnnnsuJ554Ip/5zGf405/+RCKR2N+mCiGOAe9UuWT9weV3S+GuZZB2t4Nxu8229n3P4/1/zn6H9n2pifYN7QBJFy58SmqgDyZlu8Q6LGp3JUj+7lmSxddTtnENn9v1H4aq7ewensuu9CyWH/cYp1+6ljtnr+b0nVsxXKd7GePaokxvaSfNtslI2szatKm7vz3NcijqCu0AHgXDI/HuWvenR0+jJpjRvayJDVWMq68iJ5FkVHuE8OY23tyQ/IheDSE+brT9+Dm27Xf3wHXXXcf8+fO54447mDlzJoWFhd3PPfHEEyxevJhrr72WMWPGAPDiiy/y4osvUlFRwfDhw9m0adM+l71jxw6uvvpqHMfh0ksvpbi4mNWrV/OXv/yFNWvWcPfdd6MNUG8ohDh2fe4//QPykRCZt7TBzlaboZn7/pqNWgpTB6+MUjKgeEOctsfWo377Gi1NcRqyMtgRLOLKTf9E7xq/0etYnLXtLSozh1CflU8s3c8zT/yNAHEAtmblcN4V11GVnsm0xjaeLiugKpQa2vGtgqzuGvaQ7fRbf8BVGAocLTVE5GlX/YQ/vPQgEa+f03eupyk9jSaflyafl5Blc/tvdnNHSYjrzk/j/BMDH9GrJIQ4lmhKvc+l8/uwYcMGZs2axZQpU7jvvvvQNI2qqiouv/xyysrKePDBBzHN1M6qvr6ezMxMvF4vc+fO5a677uL+++9n+vTp/Zb7/e9/nzfffJO//OUvTJo0qfvxOXPmcO+993Lbbbfx6U9/+kNsrhDi40a73T7cTdgvPh3CHmjqdRJxci48cC48sgHerYWSMEzJhxmFGuk+uHm+YlUDjM+Bmgg0xmFmIeT4YU0TfHIYFId16qMuZ5ZqDMvQyPRBug/eqITSdBieodGegEw/tMShod0h1thJenE6jXGNSXmk6vI37iayuoZNlX6C2TqjvzIWx3apvH8Z0Xk7yP5kBWm5Js1/WkJ6rIn0M4ZhP7sOVd2CNi4PZ2QRu6psjPc2YPlMhk4owi3PpaNNJzMaIbF1N24yhhH201JYSmxkBdlvrSOyoZlAop50t50tQ8ay0TsCI5JkWH0N4+ztaMDmUAmxkMuM+hX9Xtc3yk/l3SHTGL9zG5/Y+W6f5+YeN43rzr+U4xvbWJKf1ee5gGURSNpYXg9Do0nqfR4A8hIWplKszAgOeIFqOB7jS+tXMWNXJSsLivnr1BkkTA/j26KEbJubr8rkwhleeHMtDMuHEUWwuRp+8zTUtMBp4+H0cTC9Ampawe+BvK4e/Y5Y6t+gF+paYVcTFGVBWV7fRlg2tEZSQ1em+VOPtccgJw0a2yE7DIaBEEe6iPbtQU8bUncesnYcDQ6oIG/MmDFcc801PPDAAzz22GNceuml3Hrrrdi2za233tod2gHy8/MHvdylS5dSVlbWJ7QDXHjhhdx77708++yzEtyFEN0s5+CUu3yUEi7sXfm3ohGOf6TvY49tgj53BQLqq3p+/+/2nt+X1cOe8ww/X6S659N6LcHQwFFg6mC7kBNpJ+L1E/e4pMeiaBlBlvzpFkau20gImNw1f+RrfnRguBNHATv+W86i0FTQQkCI6YsXMyS5I7Wu+t048zYwihguOlWMZNMuE40W8qjCQyV+bFw0EoafWIZD8X/X4nVtsujAJMLCIcezpLCrYycbouEg6ZtskgGNV8dN49L1zwz4ujYFswFYl5NFRUs2w9qbAXA0jWdGTwBN6w7tFc11zJ7/FOMadjNv6Fj+etw5rCvIZ0260R3S630mAcfpF9on1u7k0g3v8ulNGxnd0AkYXLZ6Jedt3sD5V36V9ekBprZGWfjTlzl/3kPoTtf5n7x0aOhVv/X8sv4bMW4IjC+DpxenSnQ8BsS76rA0Da49G+6/PvX731+H7/0dmjtTz/s8qTc5mkwdBMQtKMmG+66Di44f8DUTQhx9DvhKmmuuuYa33nqLe+65h8rKSpYvX86NN97IiBEjDrgxyWQSv9/f7/E9j61duxallJTLCCEAOH5u//IG0aN37N9zjGN35cimUHr3c+2BIEUNzYxat7H7sT3fsiEn3rMMDNaEjuspL3E6u0N7ah5FgFRv8W5G0khp97zpNGKQOjuiowg4Mcqbd+MSBlxMIiQMH+8VTe6zDY1ZGewKF1LY2cr5K5aDm0OEJEGau9e7OaecTdnDeKF8CG+XTONH7ln8zzvvcNmqlSwuLub5UeO7lxewErw5938p6WwBYGrdTqbV7OSMq27uG9I1jZjZfxe5LTufrESEaQ0bcDGIUgQYnLZzO1Orq1hWPIQWN8ptrz+ErnoVbTW8z0UXe6yrSv10v+C95lcKHngFPj0tFfC/el/PkJUAiV4XWuwJ+7ub4bI7YNefU73wQhyhZDjIwTvgIQ9M02T27Nk4jsOjjz7K+PHjueqqqz5UY4YPH86OHTtobGzs8/iei1mj0Sjt7YP48hNCHBNWNH7wNGJwOn0fXJMdNYLYmqf77yy7aZ+72xZ6zrYaWITo/92td50l0EmiATGPH0fvH5b9JMikg7AVR3dM2imlkinUMoJdjGde1kx+dvJU3i4pAMDRdf5w/ExeHT2VbdmlfZZ13tZV3aF9j9N3rSeciNPPAJWkEW+Amz75FVbll6Lj4KGz+7lwMnUq5fjqjZjqEF1psWgTvLqqb2h/P7EkvLX20LRFCPGR+1BjlYXDYbxeLwAnnXQSxoespbviiitIJBJ873vf47333qOmpoZXXnmFX/3qV93lN/H4AF+uh0Fzc3OfkW46Ozvp6Ojo/juZTNLU1NRnnpqamvf9u7a2lt6XHMg6ZB2yjvdfh8HRVypzpAonYx88jdOJ3+2Zrs3I6jfNnnfEpKcH2MHAwtNvWg2na57UviMz3kZWrG+o1h2Xgkhrv3l1oIN84mSwMysdW++7O+vweYmZJgWJJH6r5zoIS++/n3LR+OLKDf0e7z3f3l4dNr7PNlSmZ7CgdBgAW7IL9zXbhzdlOFTs5/IrCo/q/+eyjkO3DnH0OaCLUwGUUtx4440sW7aMsrIyqqqqePjhhxk+fPg+5/mgi1MB/vnPf/KnP/2Jzs5UL4ZpmsyaNYsFCxawbt065s2bRzgcPpAmCyE+Zq572ebPqw53K45+pmMTxGHHrdeTFYu877QNZh5L02YS1wOYrs2J7QvJcWrRSAXgWoZQSBWt5LODCewpusmlilI29OmhV0CHUYjXAZM2TGI0+7N4pfwMatKKCMSSVOysp7ythgB9h1q0MYiTKqPcmJfNhdd9sc/zxR0RblqxgbjPz+asdN4pzGFnWpCStiZW/vUWcmI9gWbB0Mn8btJn2JkZpjIzDa/jktsZpcPvYUdO5oAXpz736O2cv2UlO4LlLBgyip+d9Um25OQCEIpEeO6pezijcj97ujUN0gPQFh34+UtOhH99N3Ux6gX/O3Cd/N6uPgv+9s39a4cQH7EO7buDnjZN3XEIW3LkO+Aa98cff5wlS5Zw/fXXc/bZZ3PFFVcwe/Zs/va3v32onvfLL7+cz3/+82zZsoVkMkl5eTnp6ek8+eST5ObmSmgXQnT749kGf1519Ne5h0347Eh4ZSfUdmW2wiCUpUNxCJbVwu5I6vLT7otNgT1b7tVSA5CEPHBiEegaDEnT+ESZ4rntGpPzoDissa1Vke7TaIsrCtZupm5XBx2jyygbm8v5FT6yvnI3fP43OBtr2Z1TROPYEZR86xQKFq0h8chCEq0OIRyOj66hxhzCMLUNnx4j7qQTwYejechQ7TQxnGYzDcNsJZDQMHHx+WxarRy8jo1rKGI+D63BdBp8BYyu2Y3h5mJ5NKqDmWQ2RPHW1xOig/RYgg4jgN9Jdod+hSLZ64RxRWMrX3t7BQ/NnEDcY5LfGeXL67fTkZaq6x4WiVG6tYq5I0t5/l+389NP3cQnNy1kSGst6wpH8IuTzmdjeqh7eV7boSZz3/ua87as5Piqbfz41C/w6PgTOG37KnakZ4DrktnZwRc2rWDFn37IGWoDPLsUTh4DQ/Phfx6E9VWQGYJpFXDyWJhWDn9/A9IC8N0LU889tSi1ovyMVM27z4TTx8OkXh1jz94ML6+ABRvBdmDGiFTw39UIpbmwswEmDYMzJhzgp1IIcSQ6oB73PUM/Dhs2jDlz5mCaZndv+je/+U1mzZo14HyD6XEfyLp167jqqqu4+OKLueWWW/a3uUKIjzHfHTbJI2Hg9gFcWA43TdUYna2xpQViNpxfkQqcy+tcKtvhlCEaOYGP14VZlmUxZ84cAK6++mo8nv5lMntTtkuyqhNPcQi9I4p114s0ztvFyuoCFodDzGjaTXZMYVgurYSImAHSknE6g15sDBxdJ+ox2DYsn+xEguasTJy9Li5dlpPB0gwf0yIuGV0XfsZ0jeeKslH7M+iBUn164X22xWOP30lBpJ03R03hs//6IiOLvYNfnhDHOOlxH7z97nF3Xbf7otTZs2d3155fccUVvPHGGzzwwAOcdtpplJeXH5QGJhIJfv/73+P1evnyl798UJYphPj4+NM5Gle/dOTVun9mBDz9mZ6v2LL0vs9PKdCZUvARN+oIppk6vmFdL1JOGp7bLqUIKALOtV0q/7Ce2kd3EDy9gO2VEbwLNtIYcPjDqZ/ktNVbGVndSMTvpc2j0+EPExogiLsaNAVC7FAxJnWkSm/ihr5/oR36lc4kTA8XX/Z9PlGh88rVARn5TAhxyOx3cH/44YdZsWIFN910ExUVFd2P67rOz372M770pS8xe/Zs5syZg2EYbN68mTfffBOAVatSxajPP/88K1asAOD888+nqKgIgK1bt3LrrbdyyimnkJ+fT3NzM8899xy7d+/mpz/9KcOGDfuQmyuE+LiZNdFgSJrLj99xiVmpmxFdMkrnlEdc9nVp4eIvwdmPQ4e1jwk+pJAHHrtQbnxzsOimzrBvj2fYt1MXhI4Hmn/eSfI3T7Nqx1BenzyF16aMZmRDNdd/pZDm7DDrn6+hbUVPvb6taTh2O8rMZ2NGOi2+JKWxBAldR1PqQ/W4AwQ8Gq9eEzwYmyuEEPu0X8F9+/bt3H///UycOJErr7yy3/NDhw7lG9/4BnfeeScPPvgg11xzDRs2bOD+++/vM91//vOf7t8nT57cHdwzMzPJy8vjmWeeobm5mXA4zJQpU7jtttuYMEHq9IQQAzt7mM7Zw/qOKvKlcS7/WNd/Wq8GxxebNN+kuHm+w59XgtvVYd/+AUE+7IHOXtNcXAGPXWTwRqXiN0tc6iJw5Tj44UxDel0Psexbzocrp/Oj7/6Ly1ZtpfWcUzn+trH4clMXrF54UhoPPlLP8n/tQHMdluRksLs0n4c+5+Gb/7VpsTWU6dLQVQfffbeq3netGiRNKY4f8qEGaRNCiEE54FFlhBDiSNYUUxz3d4fqXoOk6MDaqzXG5AzcG/5GpcujGxQ+Q/HCdtjWmrogdHw2PHaRxtjc1HyOqzB0Ceb7ciA17odSQ0TRmXAZnt3zvu9qdfjSbTt4x5+H2jNEpEcHnwEJG6x97xo1AF3DazsYriKQZrDx2wFyQhLehTgQUuM+eAc8qowQQhzJcgIa268z+M9WxZIaxYlF8NlR71++cmaZzpllqd/vep/pJLQfXfJCGnmhvu99aabB/Dsq0H7ScwMlLDf1836U4iJPK+0FmYwvNrhkoo/Ty6UsSogPQ+6cOngS3IUQH1teQ+OSURqXjDrcLRFHqryQRsP7D13fl67xzOwhh6w9QgjxfuS8nhBCiGPWM1f4Mfejs8+jjtCxR4UQxwQJ7kIIIY5ZJ5UZvPsN36CnL450fvBEQoj9pO3Hz7FNgrsQQohjWnG6nhricRC+MPzov1OvEOLoJcFdCCHEMS0/rDOteHC7w+9fU3KIWyOEEPsmwV0IIcQxb8k3Anxz5vuMDqMUPzrNJD8sp+qFEIePBHchhBDHPF3XuOdCP+WZ9C+bUS6/L63ll+cOvhZeCDF4Cm3QP8c6Ce5CCCFEl8XfCPLJIQ6l7U0UdLbyqe2r2ZqzhO9eP+JwN00IIWQcdyGEEGKP3JDGi9/IADK6HpGadiHEkUOCuxBCCCGEOGykBGbwpFRGCCGEEEKIo4AEdyGEEEIIIY4CUiojhBBCCCEOIymVGSzpcRdCCCGEEOIoID3uQgghBlTbYPPy/AgNbQ7NcYXf1Ljsk2FGlnkPd9OEEOKYJMFdCCFEPzt3W1z/i3o26SZhV1Fq2QAsXBzlM59O47rPZXzAEoQQQhxsUiojhBCin3++0M4m08sQ2+kO7ZDaaTz1QgeJZOruorGE4uXFUf76RCtPvdyOZbmHqcVCiKOV2o+fY530uAshhOijrcPh5RVJxsSdAS8ZMxXUN1nUtyv+584msBUx08DVNO54KsL1F/g/8jYLIcSxQHrchRBCdFvwXoxz/m8DmzFYFvCxzWvi7DWNDeTlGHzzzmYqNYNIV2gHsHWdP/43ge3I7kUIIQ426XEXQohj1IYGl4dXOmyotdm1KUpa1KIz6rLJ4+ueJqbrJDWNMQkLBcR0jaSu8+XfNbHZY5KuFLbTd2fiaBo7GocwoqDyI98mIcTRR+6cOngS3IUQ4hj09Dqbz/0zQff4ySqArgx8Phev27eStNEwsLEwAb+riOmwoMmkKeShSdPYqRQjIgmy7K6+eaXQNBshhBAHl5zLFEKIY0hjRPGzVxJ8bm4MLBdsF5zUBaVuwEvCa/a7AEyn5/YolgY7PAbVmpaaVylcTWN70Ns9n6YUT9fN4J/bTiUSk4tVhRDiYJEedyGEOEbUtLuM+982Wj1e8HtAqVT41rTUD+D6PCSUwp/o6TEfnrQwABdYGPATMXRwFbhOahleE0vXMVwXV0uF/KCrMU8vZdqvomz9lW/A9gghhNg/0uMuhBDHgI2NLp+6v5NWvx98JugaGDp4DTB1MLTuPULCa5LQNSxNoyxhUdRVAlNjGqnQ3puT6nX3Oy5ptpMK7wpMpRgSS7JN8/Dg650f8dYKIY4u2n78HNskuAshxMfcH5fYjLk7zirLmwrpvWlaKsTvCfJ6ascYNw2iHgN/VwFMxNCJmMaAyzdcxXEdUTxAlqPIdBx0pchNWqBp/PKZGL94vONQbqIQQhwTpFRGCCE+JlbWOHzuSZvtHaDbLr5okhlDdBZWk+pZ9xoQT9WcG5aDP5LAsF1sj0Es7EMZeqo7p6sXHU1DAxxSo8tkuAqPUlhaT69XhuNyQlN7n52JCWRYNq6uc1ZDGzFd45H5cbSAzk1nBkgPSZ9Rt84YrNyROmiaMQKMgQ+OhBACQFNK7deNqO69917mzJnDLbfcwsUXX9zv+RtvvJGlS5fy4IMPMmbMGBYtWsTrr7/Ohg0b2Lx5M5Zlcf/99zN9+vQPXFdjYyOXXHIJnZ2dfPOb32TWrFn701QhhPjYsxzFve85PLZRsXC3wojbBNviaK7C9hpojovpKDoKw7geI3VBatIlvSWC1uvb3zF0OrOC4CqMqMWYSJQMy0FXEHRd2rweAOIaVBkGMU0j13YYk0jiGaBdDhDVNbxd62g1DdZmhvDGLU7LUzz+g2x8no//aW/luOC6aJ6eQxtn/mZqv/hPmiI+spwqSiLVVPuLaPOm05JXSOcJY7GGFTL5c0MonZp1GFsvxEejSbt50NPmqF8ewpYc+fa7x/26665j/vz53HHHHcycOZPCwsLu55544gkWL17Mtddey5gxYwB48cUXefHFF6moqGD48OFs2rRp0Ov67W9/i+vKiARCiI+/1Q0uhgbjcj+4N/rx9Q7fn+eyqy3VMY5JquTF1HDCXqykgy+SxJPsuXWSYTmp4O7R8cStPqEdwHBcdMeloiPGiLZonzpKBRiui6Pr+BWMsB00pci2bLSu5/f862igq1THfdhV2F3P5dgOY9sirM5N45WoYuSPWrn9UyZfODvtQ71ugxWLOHj9OralqN/cifHKFpxt7XScPIzWggyKks3kb9uGXVbC28tg2y4HN5Ik341RelwGQ2dkk5ttokwdz6hsPIFUz3ikJorVaZGerbOzViOnuZFQawf6aSNwvnAfzsvr0HBIeBTLiyfTZmSS8HjJjxWSQQMF0XpezD+XVm8Wtq6R0E3cpRHUih1seaUepcOw9DhTT0ynMb+Y0ZcMIS3fB5YNsSSkBz+S108IcWTY7+Du8XiYPXs2s2bN4tZbb+W+++5D0zSqqqq46667GD16NF/96le7p7/hhhu4+eab8Xq9zJ07d9DB/a233mLevHl885vf5O67797fZgohxH5rjCr8JoS9B9YT3JZQuAocV7GgGk4fkgqzD6xS/HerYnOLwk4qTBPqI+A4Cjy9IrILuBa6gnQv+H3wpXEGDTHFwytsXMvF0DRcr47S9NTCdUDTIWZDPDXKS9xrEvfoeK1UuYzSNHxtCWyfiTL0VIAfwOiWCMMj8X6Xf2mkxm+PagqlaWhKoVxFjcck3XbwKIUBdHhMHE1LXazquoQdF8+ezQKybAePgqjXZJfX5DuvxNHtdsaO8DG82MSyIRjQeOmNDhJJhakp4uhc8qk0ojGFYWiYBkRjisyM1DZ0tNv4fDp6e5yWlU0YbTEao+A/sYT0bB9WzOEft26hpt6hrHkXp25eTH5LG1v00dj4CN23gY5cD0mnkx0RL22BWhZNG43hswjFE3TE2il8+Wly2ppwlcYuTxnPTT+TEG2UtNZSWlNLPJlBpt1ByOnEpo2388bT5ltKUYdDWkYhuhHnX9MuwTZSI/mUVTcwJN6B7TfZHKyg1ZuVeit1l4BtEfEF6MhK7379t0bDGHO2UtEwj8fum8CJdYsY2bwdj2sT1QM0ppeS+ZvzSD9zJIwowoq7WHGHYLoBb66Dd9bDGROgNBfqWqE0lw5/Gv7ONjyFGeD1pA4EGtqhKKt7hKFYzMVxFOGwgesoqhc2ECoKkFUx8MGWlXSJRl0yMk2i7RaGqeMLSumPEAfTAdW4jxkzhmuuuYYHHniAxx57jEsvvZRbb70V27a59dZbMc2exebn5+/38iORCL/5zW/4/Oc/z7hx4w6kiUIIsU872hQ/mu/ybq1iRqHGaUPgh28p2pM90wwJpwZfOalY41en6pSkaTyw0uXXS1yqO1MVJzl+CHmgPF3xViXYzl4rMugaCGFPFNZSYdvWQFM938CalhpeUQcMAxdoVQo6FXcstKHTwkg6pDkOOqlFJAIeEiFfqibFsiHa64ZHCQf8Bsmwh2TYl6pZ1zQm17SlmqQUdQ6096qnLrZsypNWn+YnNA2PUl3juCs012VZKIAFKL3rgEMpyhNJim0nFdq7tiduGHhdhVep7t54XcHUxnZ2h/zsTPNTE/Tx1yca8TkKj+vgaqnXyFSgo7A1jYVZaXzjjTZuWLmQs7evpCY9n9dHnUjCMPHbSVzNwNUMTNcmPd5JVjTC7qxC2l+uIxBPMHrnbvyxBCVeD3ltHQQaYX1oNA3hXADSrHZG129lO6MBCCZcTp+3AXtYlHO3v4HXtbreOQMwGJPcSP7y3WTHmgGwMbDJQMODpRk8OfpsIt4AAFXp+biGhu31EozE6QzpTNq4gxGVdbSFAmwqHE3U5yWro4UxVbWEu86QvDVuTL/PbCTDz4TKtYxfuxYbqAvnEfMFKG/ewZDWzUS/XssucimmBgMdDYVDnNSnKfX6u2g0hLJ5dMrnaQrnorkOo2q2Utq6m5nV7+FzktSk5fLAiV+kJq2QuM+Hq2lUVNUxYfVOPLaLAlpywmydUMrQzfUM2dmIbtk0FIfZUV6AoSA70sJ5a95kY/5IFow4jrM2LmNq20b8/ih2VLE1OoJWbx7BKXmU33sKaVNz4M7nYM7rqWFKv3U+XHF6v9fgiOW68Jun4aG3ID0AP/gMfO6Ew92qo4rcOXXwDvji1GuuuYa33nqLe+65h8rKSpYvX86NN97IiBEjPnSj7r33Xmzb5sYbb2TDhg0fenlCCLGHqxTnPuGwuSX199ZWxb8G+Jqp6ux5flWDw/dn6Hz9lb6lew2x1M+OpgFW1D162V47pD1/63sNbaYPNJ2ChAuWQ6ArtO9ZtD9mYXtNHI8ByQFKCi0XvF1f8V2jwRREkphdlzXlAfWmwfqgn6ShUxyL91vEivQg7abB+I4YuUkLU4ekRqqHHwg5LiHXpck0yHP6t8HSNZQLDgqlpwK531FUtMdIs2zWZIdTW6MpXMMA18Xnqu7tNJTipJYOHi/KYX5BPr949V0ATt6+jFvO/w5xTwDDdbFMkyQeor4AzaEMztq4hLdGHs+YLZVkdEQAqNjaSChmU89QvG2Q5iboSPcxKrKZKir6tNtwFOX1O/C5vQ9knK5XXic71jNCjomDTjsWOezMKOwO7d1vo6Pw2ElK2+o5Y9l8cuItdGrpPDPsUzSlpXquY4FClOHllM3rAQjYSfbmtxOp5QEeFEM6a/jjxFlsyf8yStOYXLWaLy59Da1rt556Df1AtHs+A8VzEz5NU9dBi9INNpaM4rTKBfic1DqLOho5rnojWycP7153bl07HtvtXk52UyfO2mqG7EgdvCh0cquiJM1W6kszaQll8dKEU/jq0rm4pmJtaSGn7H4dOmEDM2imAJKQWNzMirNe4IQfZeL50Zyejb3yLsgKw6en9Xsdjki/fBJu+WfP35f8DubdBqeNP3xtEh9bB3xpv2mazJ49G8dxePTRRxk/fjxXXXXVh27QmjVreOKJJ/jOd75DOBz+0MsTQojeFlXTHdoHa2UD3Lt8H9fb7Ov6fp3+oX1/aYCrukJXf4bt7MmSA8+7l2ivEhkdKLQd4l4T2zRYGw7QewurfR4aPSZJXWdlehClaRiaRrqb2t7SpMWERJLhls2YpM1AGgyDtQEfu71ezL1epryYxdD2GEZXmQ2AofpvisdVhG2Ht8rGkDBSobSstYbJVeswXBd7r3Hlk6aXcLKDjI6O7tAeiFmEYn3bmNaRRHMVPjeBGuAFHBrZOcAWDfwZ0Egt29X6L0dTilkrnuWiDW+SFU8F/rBq54ItL/b57NRmZ6EbNn4STKreiu70nL7RXYcZu1f0We7GvHI2FlXgGCbu/2fvvuOrrO4Hjn+ecWdyM0gICSOsgGELoijuUfeoWtuCCxFxoLXV9le7FLp+1rb+pFRLqZUiYotgrVqto4q4UZaIEDaEQPZO7n6e8/vjhiSXBDIgYX3fr9ct3Gec59yLvc/3nvs936MbBBw+HLT8yaf5awvrJvmp/Vr0cUda/7jnO9P6xj3vVVLV4py0kpblPZPL/Y1/L09IJ2i6ObVgFafvivU9goMK4n+Ft2oilM39okVbPPtey21Hq/37qlRs9F2ILnBINbkSExNxOp0ATJw4EeMQy1hFo1F++ctfcuqpp3LppZceUltdraKiglAo1Pi8rq6O2tqmD7JwOEx5efwwXGFh4UGfFxUV0bzIj1xDriHXOPzXSGitBEo7JHbyvENiAw69Ic2hJcs0wKmDx2wZqIcsqPCDPxzLUTHgi15Jce3UGDr73tEil5N3eySxNtHDRymJrElKaPzioSuocRgEdJ2TQhHSI9HGRZniNPv3sZSiYv/FmprRgKHV/hbbWhPSdfpXleKymoJvbyQQ+zLRSgGDxHA9ZrPtZrTlMRqg24pdnmx6kx+3z9Y16lytDRzt62H8a1fE7oP9a4pw7TdaPrB6Ly4rgoaBTWLj+50SqiEtsG/EGpQGq/oPoSgphZ51NVy+dgXjC1Zxyp413PTFi/SrKYy74vqs+HSasNnaf6Cq4RHjsKMkBWpaHJVWH/9NtmddRdzzukR3i3PCrpY/2EecTTGApiwcVgRDRQk5Yu+Pho3Wyn/JhqeV2CEhds2j+bNk/742V491THwmimNPpwN3pRQ///nPCYVCDBo0iAULFrBjx45D6syzzz5Lfn4+P/zhDw+pne7Qo0cPXK6mZbwTExPx+Zom7DidTtLS0uLOycrKOujzzMxMtGYjdHINuYZc4/BfY0yGxoXZ8SGi2cbA+DU5Gj85XW+xdhHQsIBRK9sPpSCWUhC1wVKx+t5eBwFTp/mgdchlYrnN2PUNHZJc4DbAoUHEiuXMG3pslmvDwko1CU7e6pfGugQPa3xePkr1oRreU9O2SbIsEiyLKkOP9aHhMdgfREMDTUMHBjVUk9n/ffBFLXyRKMmRaCxob2i7TteI7Hd4YN/bpjWFcpauxb1GgEqHgdIUv3v3H03nOlys6TuCqK6TEArEHZ9aX0VWdSl70tKoToxVXPF7HA35800ipo5l6mxLHUi2tpVsfQuBBAdVaV42j+nN+4MmxvXFRieAjyKy2ZA8BLvhHagxfBQbfVCAw4pw5bZ3GFC9CzSbMSWbOW/3qmat6KiGVBYLDb/D2xC0a9i6webMvrw5cjzb0jNJDAc4f+eHXLDjfXrVFwMK1fgVLkpyIH7Ee3t6P/b49p9TFqX5O6oBl256B73ZlxqlFHoo/otIyHTibZY6tSmnN82/AAQ8TraflEW02f8hLF2jpG9y4/Oxe7/AVBar+47izZPOxEbDxCKL3XHX8gxJIu0X58f/OuV2wr2XAUf3Z0mjB6+O20eim4QHrz0mPhPFsafTOe5Llizhs88+46677uKiiy7ixhtvZObMmTzzzDOdGnkvKyvjr3/9K1dddRWmabJ3716Axm+HNTU17N27l9TUVDwez8GaEkKIg3rlWp2n1ipWNkxOvXwQzF5ls2QT1EZio+sTe8eqy5zRW+OuMRouU+PDSRpz19psrVJkJoDLAEtp9PJqvLrFZldVbB4oAAo0W6H0pudx9t1wGxY6armv2Qkuk6jbpEaB6Y9gaxr2vuB6H0MDrwOqmuWqe8wWbVsug90eZ4vtJ9cF6BWKENQ0drochBsmn+pKkbJf/rrWSr811TQRFWJVaAL75q9qGjucDnpaFi5bUa9r2ChSIjYupXBFIySH6qn2JBDSTRwNee5+XafCYfLN/L1kRGyqEpIoScng5VEXMqRuD4apE40qtKoC9KhFVk0Jwwu38a8Rl+KKRKnolUSC5Sdou9kxwKR3YQ2eYITyHol8OTqbkMskZ/cunCpCrtpAfRI8P+46XJbFRm8mQUeYSWvfBCCCh1qXmxeGXciY9dsYzDa2eYawMWEEStNI0GoYU76JCCZbMweRHqjktKKvWqnQE3svd3kG4KoL4/CEqfLG12r/su8Aqt0e0gu34LFCgI0NlNEbh1HP3pRe5CdlY1gW1r77rVLkJ/eiT7gQyzIIo6FsHYdyYGChK5uw4aR3VQFXbXiN94acRb0rgbSySt4ddB7l1VkMqNnBmtxxVGQNJDNSR/LuEpKiEfpqQep8bpyVQSzTIH9UHzzjMuCqXvRavY3QhiKKNRuVGCEhGmbC3i8YWZjHh0Mm8uGwMzmvJp/Q+Wfh0usYjIan1qZaS8V7QX/6fm8ERk8PLPs5/O3dWNB+18UwZiDHjJvOhYxkWNQwOfWey+CkPke6V+I41eEFmAAKCgqYNGkSAwYMYP78+ZimycKFC5k9e/ZBF0rad0xrCzBt2rSJG2+8sc1r/+pXv+KSSy7paJeFEKLbRCyFw2gK2WylqAwofr8yFrQNT4PffaYoqleEwoqqSNOEVUNT9PVqnJsNPVwwMFVjUDJ8Var4xfIo9VEtNhJvExuy9u4XnFcFY2kyEBtt9+6XQhG1oTocF3hrSpEZDFPqdICyie4XkI+vD7X+8+y+1VWVwhe1cDa7nQQ0jS1uZ1xbzdvsYdvcNNHFg99Kxmz2k0c0quKeF5eE8XoMfL52DAhFotCw0FGgPoq/OkpqpouVS3ex4vViokFFr692YQb81Cd4qdN9mJaNL1DNoLpdKKXzVc+hrBk4GMtQnFS4h2GF+fSpKaRe81IZysAbsHBrFj16WKRZe1E1QeqtZCK6k7IUH3n9+lDn9TC8ZAfnbvuc5glAdZqLWuWhF7tZnnY+lY5UKtMSqUuOr8XuiEYZWL6dC7Z9TBn9MIkSwEc6pYDBLncvqntn0NOoYn1SH8jycfZvTyczNyWuHTtiozsOKSNWiG5Rpv2k3cemq191YU+Ofh0O3G3bZvr06WzYsIGFCxcyePDgxu3Tpk0jLy+P5557jkGDBrU492CBe11dHStXrmxxztatW5k7dy6XX345F1xwAcOHD+9UiUkhhDgefLY7yt+/srE0WFkMn+xVjVVesFUsaK9ryLN26NDDHR/Y10di9d73t+9W0ErO+FB/iFS75a0iMRJtrFKz/8+3FlBvGPh1DU1BnaFRaRokWhbJts3D1ydyzYXds/hSC9X1ELWJeDxEAhbetKb0An9BNZ//fh12koeTvjmUrOE+tK2F+HfX4jh9MA5v+3+oVv9ei/Xr16CoBj3qh0SNZfoZaLuq8GmV7EgYTMjloKR3Sty/0UmlW7l464dsM4di4cFzUioZ868ALNyRMOZp/dAOMn9AiGNNqfbTdh/bU/2yC3ty9Otw4L4v+L7vvvu49dZb4/bt2rWLyZMnM3jwYObPn49hGGzZsoXly5cDsG7dOj7++GOuvvpqevfuDcAVV1zRIm+ruZUrV3LXXXcddCRfCCFOVEopNpZa/OgD2FRusWmvDaFobFReEct7dxug67GgPtRK0B5rKPZnK4H74ECIjIiNrcePxPdoluu+b/XUfX8P6npTXXdiPxBsS3Ax99YErhrTFCifiPauKKXwvSJKntmAv8amOslLXZIXpUF2ZSGXXefC/ZtvHeluCtFtJHBvvw7luO/YsYO5c+cyatQobrrpphb7+/fvz913380TTzzBggULmDp1Knl5ecydOzfuuFdeeaXx7yeffPJBA3chhBAHpmkawzNMXr4ewGR9icVtL8GuasV5/TS+MdrBjYuC2JaNrcUmmMZy7w8wI7dh5dN9EiMWhaaDXU6NwaEwKZaNQykSLCsuf9siFrBbuka0YSJrcyENcoe4TvigHaD3hJ70ntATfjgqfkcwHKu9r8touhCidZ3KcRdCCHHs+LLEZvLfA6wvtPEEIziUwu8yiTYEiKmRKJUNueHJkSin1fgJ6BoW8HmiNy5P3WXbXFBTH5fzXmsahDUNXyRKwDSgYeVTrWEYPqhpbOvhYe1DyfRJkhUShRDxSrSftfvYDPWLLuzJ0a/TVWWEEEIcG0Zl6Hx5fwK/eS/EQ28BgQieUBQFDA+G6GnZfODzUuMwGV4XwKkUTktRYhotJpeGdJ1qQyfVsrGA1T18lLhjdbq9UYvhVfW4bRulQLdtahIMLry0By+d5STLJ0G7EEIcCvk9TgghThA/PM/FqD4mgSQP9S4TDY2eDaUeT/KHwLJJalb60dPKhFSUwt2wPT/B3Ri0A/hNg52JbpRSVOoaAY/Nsw+m8thlLgnahRDiMJDAXQghTiDrZnh4ZZKDfhlOwqZGZUN1kgzLIicYii2+1MBn2/QNxa8E2isSxdOQYVnlaPmjbZ1p4LJsrhr8KbeOfJOhWYe2orYQQogmErgLIcQJ5qpck53f9/D3qQlsS3BSaBrUaxrJtmKr20l9w8RVBfRoWE0VFDqKMYFgw9qd4IlEW7SdEoliAH28lRiaTKESQrSH1oHHiU1y3IUQ4gR17Qgn1z6eztaSKLVBSDQVp/6ujveSEhhdH2RTgpuoBpam4bBtRtWHcCgIaxrnTfQQ+shPldtBuSuWLuOJWuTW+HF7wGG2DOqFEEIcGgnchRDiBJeT0XQreOd2F9P/XMtOt5OQYTAwECI7GMZj2+xLeqk3dMaNcLN6Y5hxFbX4TQNL00gNR7E0+N33U3jvzSPzWoQQ4ngmqTJCCCEanTLCy2dPZOBuyGPf7XKCRmPQbgGbEtwMGehk6WOZDB3gwGnZJEQsgobOvTenMLC31GoXQrSf6sDjRCcj7kIIIeIYusYQFaFIOYjqGh+kJJIZjuJUiiKng7G9dQb3jN0+/viTXliWorbeJiUpFt5HIpEj2X0hhDhuyYi7EEKIFmbemsx51bUMCIToGwhjA1WmwWUDNd66OyHuWMPQGoN2IYQQXUdG3IUQQrRw3jgPY3+XyWdfBKgJKDJ6Gpw+0oPDlKoOQghxpEjgLoQQolXJPoOvnZV4pLshhDjOKSnz2G6SKiOEEEIIIcQxQAJ3IYQQQgghjgGSKiOEEEIIIY4YSZVpPxlxF0IIIYQQ4hgggbsQQoh221oQ5ow5dVzwtyCFdfaR7o4QQpxQJFVGCCFEu1z+RBX/DXmIGG4IKnr/yeK5y21uHCG3EiHEoZBUmfaSEXchhBBtqqiJsqLKJGI0LLSkaaBp3PSajLoLIUR3kcBdCCFEm26fU0VlgquVPTJSJoQQ3UUCdyGEEG3aUx5FaS2DdE2pI9AbIYQ4MUngLoQQok3bfF5oJUhXKHaURo9Aj4QQxwuF1u7HiU4CdyGEEG2q9Lliee37MwwG/U1x0SOl3d8pIYQ4wUjgLoQQok2+iNXqiDsAmsa7niRefq+uezslhBAnGAnchRBCtMkVjOKIHriCjAn89dXq7uuQEOK4oTrwONFJ4C6EEOKgtldG8Rs6Z+QfOB3m3O3FbHK7u7FXQghx4pHAXQghxEGd99cQF+0soSjZe8Bjtqf7sEy5pQghRFeST1khhBAHVROE7OoAAdM44DHbe/gI2AfeL4QQ4tB1aeD+5JNPMn78eF5++eVW98+YMYMJEyaQl5dHKBTipZde4oEHHuCqq67izDPP5JprruHHP/4xO3bs6MpuCiGEOIgMzSKqawwtrz3ocXt9kiojhOg4KQfZfl0auE+fPp2cnBwef/xxioqK4vYtXbqUFStWMHXqVHJzcyksLORXv/oVVVVVXHXVVfzgBz/g4osv5tNPP2Xy5MmsXLmyK7sqhBBiP4GI4v7/Rtlquqh1OvCbBmP3lLdeXUYpEiJW93dyP3b4yPdBCCG6iqZU1y57l5eXx5QpUxg7dixPPfUUmqZRUFDApEmTyM7OZsGCBZimSVVVFUVFReTm5sadv337dm688UaGDBnCs88+25VdFUIIAZT5FWMWWOytb9igFA7LxmHZ9K+qZ2OvlJYnKUViNELJd5zs3FrLf/7zOt6Uem6//TYcDker11FRGzTQDB0VjqI5zQ71s/axt6l6bRvJ95/B0x/pLK5PQ1NQr0FSNEpU19nl9ZCUAB//KIX0xG5M5YlaUFgBE34IVfXw4DXwi8ndd30hjiEF2i/afWxf9bMu7MnRr8sDd4B58+Yxb948fvCDH3DDDTdw5513sn79ehYuXEhOTk6b5994443s2LGDjz/+uKu7KoQQJ5ynPo3www9t/ErHbvwluvWfpDWlUK0txKQUowsr8Vg2I0qq8YWjcSPzZiiCw1aM3LWZs7d8SqnZH4JONMBJiGSKqCATCxcKjYDTIOx0gAZhF/SoDhAxDBxBm17spid7iJoaG7JOIqoZrOibzfyTL6DKMMgJhbBtWJ2cQL1pgFKYUQtPOEr/2nqSTQ3LdJCIzWmOKjw7CulXUsC1a/9LUiQQ/7p0DVwOCIZjtei8zthCVP5w7PXpWuytstp5K81Igs1PQnJC+44X4gRQoP2y3cf2VT/twp4c/To2vNFJU6dO5f3332fOnDnk5+ezZs0aZsyY0a6g3bZtysvLSU1N7YaeCiHEiWXBF1FmfABoxoFi9TitBu0Amsa63j0A+CIzhUlf7iIpFG3cHXU7Sasu5obNL7Ei6SwSKptGv8O4KaU/+zqgAd6whbch7UXVQsjpwBOMksYe+rOpoVE4ffdKXhp+JUMrainVNUaEwqQFw7yblhIL2hv6FnWY1Jom671u3JZNn1CYUmC3ncGgXsmcV+/Ht3/QDmArCISbnvvDLfd3REkN9LwNAn8HQybzCiE6pluqypimycyZM7Esi8WLFzNixAhuueWWdp27dOlSysrKuPLKK7u4l0IIceL54XIrNoJ8GAUdJutaSacZXLYTW9NRIU8rZx24DxrgCse+BPSgsMX+kcUb2NArF68CXSl0BRUHSbsJGjpBPXa9qK5RbRrcvOrf3TftLRKFt7/orqsJIY4j3VYOMjExEafTCcDEiRMx2jHSsHbtWp544glycnK47bbburqLHVJRUUEoFGp8XldXR21tU8WFcDhMeXl53DmFhYUHfV5UVETzzCW5hlxDriHX6OprhK2uCVcDjpaBc8DpIWQ4iXRipHlfL21anhsxYjn0zmgE07bRAV/04JNUm4+T20Bq4OAVcw63SFlN49+Px/+u5BrHxjWOFlJVpv26JcddKcWMGTNYvXo12dnZFBQUsGjRIgYOHHjAczZu3Mjdd99NUlIS8+bNIzMzs6u7KYQQJ5yfvhvlV6sPf7vXbNzNgCp/3LY+VXt58N25/H3ot8jdXNahW7ClgaHARzk5rGk810Jn0dhv4g5GWJfQk/+cMpIRdQHyvW4+SvERbRhZbx6pO2ybfsFwrA2lGBwM88P3FnLBts8P4RV3UM0i8LX2y4MQJ57d2q/afWw/9ZMu7MnRr1tG3JcsWcJnn33GtGnTeOyxx9A0rTF1pjV5eXnMmDGDhIQE/vSnP0nQLoQQXeSXF5hc18+OTbRs/ugopXBGLZIDYS7YVhQftCuFHolS7k7j9YGXcnb+R2we2KMhqFaAwiREbOw7dm3V7BF06vgTdYKmQQ1pbGI8pWSx25PNayddQmpFPcEam0u/2MkjL7zDdkOnjz/I1cUVnFVRw5jKGhzRKJptkxQMkVtTh9uySIpEOa2qlJyKvXzRewh+03Wob2f7rHhUgnYhRKd0+Yj7vtKPAwYMYP78+ZimycKFC5k9ezb33nsvU6ZMiTt+X9Dudrv585//TN++fbuye0IIIfbz6tYoV/+LWADf3vx3pTi7rIw/fzuBf/61gLIyP6m9S7ntvrPI6puA6YgfJ7LDFtVri4l8kY+R6aM636LXmZlEt1dghxWe03qj6WAkudCTnKiIjeGJpd/YlqL49x9Q+Oh6IlFw9Y/wse3js6y+lPZIpzq5J3WGQQRFVnUdqYEInvMy+Ot9PXEY3fBTu23DTxbB71+GiB3bNuV8mHc3tJJCJMSJTkbc269LA3fbtpk+fTobNmxg4cKFDB48uHH7tGnTyMvL47nnnmPQoEGABO1CCHE0+aDA4vIXFXXhhttEG0H8laFKXv1JTyKRCPPnzwfgttsOXMf9cAuXBfngsxp6DE9i7ABZxVWIY0V+BwL37BM8cO/SVJlFixaxdu1a7rzzzsagHUDXdR555JG4lJnCwkJmzJhBTU0N11xzDevWreP111+PewQCrZTqEkII0SXO7mtQe79Jmmm3Kx99R+TIjiY7091ceHmGBO1CiONWl33K7tixg7lz5zJq1ChuuummFvv79+/P3XffzRNPPMGCBQsYPXo01dXVQGzBpta88soreDySFyiEEN3K1jBsRbSNNJOaSDf1RwghTlBdFrgPHDiQjz766KDH3HTTTXFB/cqVK7uqO0IIITppTEKU3TuCbOmZdOCDlKLQ102TO4UQxxUp89h+3VbHXQghxLHp9alOxhRWtnlcwr6JmEIIIbqEBO5CCCEOyuU0+Srdd9Aykb5QhMyo5MoIIURXkrpUQggh2hR1cdCqMqfnl3Ht7bLmhhCi4yRVpv0kcBdCCNEm3Wm0vt2yOb2gjLxeKdw9wdnNvRJCiBOLBO5CCCHaVGK2HpQbto3jjAx2TWo9sBdCCHH4SOAuhBCiTZVuR6srqUYMnfcmy61ECCG6g0xOFUII0aYETWs9x72N1VSFEKJtWgceJzYJ3IUQQrTpwdONA1SVOXClGSGEEIeXBO5CCCHaNPNMA7T9gnSleHiC3EaEEKK7yCeuEEKINmmahv9+B5f1sdGxSdRs/nW1xqxzJL9dCHFoVAceJzr5xBVCCNEuHofG65Ok5KMQQhwpMuIuhBBCCCHEMUACdyGEEEIIIY4BkiojhBBCCCGOGCVlHttNRtyFEEIIIYQ4BkjgLoQQ4qDqwjYby22ittR0EEKII0lSZYQQQrTKVoqUP1jURpq2/etquGao3DqEEIePpMq0n4y4CyGEaFXGH+ODdoCvvwJhS0behRDiSJDAXQghRAvlfpvyUOv7Fm20u7czQgghAEmVEUII0cyU16Ms2HDwY+pDMuIuhDh8JFWm/WTEXQghBAALv2o7aAcoCXR9X4QQQrQkI+5CCCEAuPOt9h23qujwX/v2fwZZuCqMbcOYviYf3uHG45SxJSGEaE4CdyGEEAAErPYd5zJablu3OcTMp8qp80OvdI1xWW4SXMF2tXfp/Hre3GuC2wmWYnWxRcav66md6etA74UQ4vgnwxlCCCE6Zr8U97ztQR78XTm1/tiuojLF619eQDDsaFdzb+7WIRCB6hDUhcFvURdQ5JVED3/fhRBHHdWBx4lORtyFEEJ0yObK2J9/Xxvh3R1Rtr1VSctBeJ0vCoa32dbzb1UDTgjuN9xvKd7baZGbIbcpIYTYRz4RhRBCdMhXlQrf/1RRZ5qgaThSfIyqrqdHtKlMpALyq9PbbOuu5YDWeo5OD03KTgohRHMSuAshhKDM34EgOWpT52hKg4noOmYrv2FXaO42m3JEomC0zNrUlMITkVQZIU4MUg6yvbo0x/3JJ59k/PjxvPzyy63unzFjBhMmTCAvLw+ATz/9lF//+tfccsstnHHGGYwfP56VK1d2ZReFEEIAs1d3LHBvLiUUIcmK36YBzqjGnD8Us3dP/EpOtq2oKI0QCkUZV1iG17LwaSru5J7YjBraduAvhBAnki4N3KdPn05OTg6PP/44RUXx9cOWLl3KihUrmDp1Krm5uQC88cYbvPLKK1iWxcCBA7uya0IIIZpxt1Ip5oAc8QdXOU12epwtDkuwbDZ8WsO9P97LS69WYts2L/5xJz+6dg0z79nKgzdtxnY4mbTpc2p9bvA5IcEBiU7KfR5+8PfaQ3xVQghxfNGUUl06STcvL48pU6YwduxYnnrqKTRNo6CggEmTJpGdnc2CBQswzVjGTklJCSkpKTidThYuXMjs2bOZO3cu48eP78ouCiHEccdWij+sslhdAg+frpHTIxZs+yMKXYsNmt/2hsU7uyBiQV1HslIsG2oicZsM2+a67Xup9iVg67ExIWcoTG+/Hxuw/CE29UxlrD+IqRSabZNSVQdK8adxQyhNSmhxGYc/RODXSRh6535Gt4IWVT97A/whUh69AsPnatd51c9+Remdb6GCFu6kIH1c29ETHPDUdLjslE71RQhxYJu137f72KHqwS7sydGvy3Pcc3NzmTp1KvPmzeOFF17ghhtuYNasWUSjUWbNmtUYtANkZGR0dXeEEOK4VxO0SPmjaiydtnCDAg5jvni0ZVqNpetkFxbTd3M9Hw3LoSoxgahpoiJRdmWkE/FFGVdURkYwgisYJr20Am8wRO9gMReu/5gHr7qClf37A7EvAYMqahiXX8QjEyvw2lFGn5PO+q+CqFAET10QT10ITyDE2PydOG2bHhSSQhH1eClgEE6C+KjCoYWoNzy4n/onYbxsZyjV9MDAIp1qfNQS1YIEeqVSn9YHY2s1yaFqkqjHRS1JNXti2belwOW/Aq8TJp0NxVVw/5Vw0ZjD974KIUQbumVy6tSpU3n//feZM2cO+fn5rFmzhhkzZpCTk9MdlxdCiBPKwL+orq133Ergrts2PesDuCNRTt6xm/dG5eKyLQaUVaCcDnrV1jPxs02kV9QBEHSapPj2cHL5JgCWvriDyd+Yzqb0fkxZv43kcGxE3+EySa6oZ8fbhfizMlBuF35XAklmLRG3g8qwzll7P2nM+0whTDKrCeDFix9bQWrUwMBiHROoJwkHYU7iC3pShIWTctWPguoUauphTKiMROoxsUigqOWUOX8Y/vpO7O//XgU/vg5+dVMXvMlCCNFStyzAZJomM2fOxLIsFi9ezIgRI7jlllu649JCCHHCqQi1fUynKQXhll8LLt28i+RQGIDUOj+aUmTX1OJPTaZXIMiILQWNQTuAOxxls3M4v/7a7dx/5c0M+p/f8XFOLpfu3NsYtANEXE4CXg8Oy8bjD8Q2ahq1vkRsTeeLzJEUJmbF9UUDvPiB2E3OwKKeROpJAmAYa8hkLwY2ToJksoWBgd0kqCBJ1GJioRNGpx1Lyf7ulQ68eUIIcWi6beXUxMREnM7Y5KWJEydiGB2ZCXX0qaioIBRqujvW1dVRW9s0kSocDlNeXh53TmFh4UGfFxUV0XzKgVxDriHXkGt05hpdur5gqGUwe9b2Aq7atKPxea3XzbCyCpIaAnAF9N9T2uK8zLIqtqf3I6/3yFhevKUodTop8sRXk4k6Gn4cbvaylKE35tLvTcxss9smETRsNGx6El8sQQMSqcRphxu36YRpl3CUwl274zYdr/9dyTWOv2scLRRaux8nui6fnAqglGLGjBmsXr2a7OxsCgoKWLRo0UErx8jkVCGE6Bztd11Y/7w6BPtlyui2ze1r8nBpOgnBIE6nSdQZX2Xmujc+JhqNz84MeB0suuZsbGD+Sf0g3PSl4KzCUi4uiAXYiVW1uIIhirN6YTfUfNcti7SySjTgyq3/YWBNfuO5+25q+9/i8xhNAQM5mzdwEf+zxB5jAGsSx3Jm9Tp0bAzCJLCn7TAhKxX2/rWto4QQB7FJe7zdx56kHujCnhz9umXEfcmSJXz22WdMmzaNxx57DE3TGlNnhBBCHF7XduX0IbPlbcPWdZaMGkpevyxWDRlIXkbPuP0RXaOspw8nTSkwBhabc2IpLsUeZ1zQDvBRZjoVLgeRqMUWj4cinw+7obqMZtsk1dShATmV2xjQELSrhsce+lNI37j2FDCUdYziMypJi9sXxsmaxDG4I0G205cgTqK4iOI5+HuR5oM3Hz74MUIIcRh1+eTUgoIC5syZw7Bhw5gyZQqmaXLXXXcxe/ZsFi5cyJQpU7q6C0IIcUL559dNRj4T5auKpm0X9IOvyqA4cIiNu00It0wjqWlW273S46Y8ECQ1FKLe6SA/MRFr3Egu/2Q1fSuqsdHYkp3JqmGD0GybStVysqvSND7qkcbJkVK+e2dPhl7Wj1X/KebDv+3CjETIyPWSGPIz+stSAolpoCl0omgDMki95ly0aBj/715Ci0awNAOGDcTu34vEvF2o3X7Ko32IaopqdyrVw3M5e8FlmAZQUEPFNU9T5U8kRG8G8wVeaptG3hd/D646DSrroXePQ3wzhRDQpcl9x50uDdxt224cWZ85c2Zj6ccbb7yRZcuWMW/ePM455xwGDRrUld0QQogTzvqpJqGooj4CPTytJ3x8UWLzxg5Fllcx9U3aMxUTjNbbSo7En72tRwq6UnjDEQZXVZOYaLLu3gt49eMShu0toSopkb57i0kvrqJk2EDIii8HrClFST8f8xYMaNw24dreTLi2935XvqBFXxqrtf/vtQd8GYkNf/baf0duDxLqf0n18+sJvLCO6NXnotUXQU5mfA13T/tqwgshxOHUpTnu+/LU77vvPm699da4fbt27WLy5MkMHjyY+fPnYxgGW7ZsYfny5QCsW7eOjz/+mKuvvprevWMf1FdccQVZWVktriOEEOLQFNRa9PtzO28HoSj4mwJ1l2UztrqeRKvZyLlSDC6vJCUSwZPm5Cd/GYbpiKXZLPq/XaxeVknYNPhP316UJriocTsh3HR+j0iY16Z7OT1HAmQhjnd5Hchxzz3Bc9y7bMR9x44dzJ07l1GjRnHTTS1r3Pbv35+7776bJ554ggULFjB16lTy8vKYO3du3HGvvNJUauvkk0+WwF0IIbpAVoJOO8fcG/LcY8e6ohZnVta1OmHq0/RkVszphcsVP1H1xu/158bvxRZbGvS9cmqcDjB0cGux38w1GFRWi623XE1VCCFOZN1SVUYIIcTRr93VaJSCqqY899PLqknc707St7SCLSlels3rh8PhOGBTnp/WEQ1bRBNd0DD5FMumb3Elj92ayqQJbUwQFUIc8zZq/9fuY4ep73VhT45+3VbHXQghxPEh0aFxwaCmXPeEej8jdhaQEAziDoXJ2VvMKdt20cvf9kpQKS5FVNOgPhxbldQfBn+EMo+bCQOdbZ4vhBAnki6vKiOEEOL4cskAWPp1b+Pzdz7syfp7N5O7t7hxW1TXsfoE22zry+94yP1pOeVeD1gNw/ZKMa6wlEEZUrVFCCGakxF3IYQQHbKmOP75hWclU37xUPzOWEpM0GGy4qRsLjvp8zbbSk82mbi7iCHlVRi2jSsS5fItuwi6DpxeI4Q4vsjKqe0nI+5CCCGA2Eqj7Zn01KOVtPOfP5ZDQWE2775dxZgRbryr/tHu615y32ASH9/EPau+Imga/HdgH575Zd+2TxRCiBOMBO5CCCEAGN6DuEWbDuRAY159s5zccksGkUiEL1e1/7ozLkzkuvEn8+ArQVI9Gv+6yk2iS34QFkKI/UngLoQQAoB/X68z8C8tVzHdXysLnR6yrGSD52+W8o9CnIikvGH7yZCGEEIIAAYk67xxvUaPNoq55MicUSGEOCJkxF0IIUSjSwYalH8n9vcD1XX/6UQZ8xFCiCNBPn2FEEK06jdnt9yWYMKIdLl1CCHEkSCfvkIIIVr1PxNMFl4GXjP28+w1g6HwHuNId0sIcZyRcpDtJ6kyQgghDuimESY3jTjSvRBCCAEy4i6EEEIIIcQxQUbchRBCCCHEESMpMO0nI+5CCCGEEEIcAyRwF0IIIYQQ4hgggbsQQgghhBDHAMlxF0II0WGWP8KXF/6bqnVVhFwm7vP7cO6LFxzpbgkhjkHqSHfgGCIj7kIIITpszbh/Evm0GN2ycQci6K9u59mL3z3S3RJCiOOajLgLIYToEDtiEdxWzaenD2Nn356gafTdW45eGSbij4LjSPdQCCGOTzLiLoQQokNU2OaroX3Z2S+jcVtB7zQq05LZua7qyHVMCHFMkpVT209G3IUQQnSMstg2KJO06lqclkXYMKhJ8FCf4KbCdjLgSPdPCCGOUzLiLoQQokOeu+cDEoNhnJYFgNOySK2tR7ei9O8jeTJCCNFVZMRdCCFEhxTmRzHM+DoQhlL0qKrFmyK3FSFEx0gKTPvJJ6wQQggA3n58E3l/34muLMbcNZSzpuW0elwpXnq0st0MRyjfXEPfk5O6tqNCCHGCklQZIYQQPHPLCnb+aQOeKj+u6hAbf/Mliyf8o9Vj06oi1DjiU2K+yOrBy+NOoqo82h3dFUKIE5KMuAshxAnOX29Rt7Ycb7NtGrA7lMzaX33CyT85o3F75bYavjypHyVJCQScGgmRCAXJCWzKSEEBN7xdQe/Fu/iGw4d7fG13vxQhhDiuyYi7EEKc4JY8upWg1vJ2UJ7gYfM/vorb9trSIurcTtB1dqUlYegmuRV+Ti6qQlewLSuND0YM4FcZl1EeSOyulyCEOIapDjxOdDLiLoQQJ7iKf+9gY58Mztyyi6ius3FAb4rTkkkO1FAdUuz6YDf9z+4HwNYKm4DTicO2+dr2YoyGO2mPomqcls2KvmkAlKQksnztGL7fzj6U19v88XOLPXWKRK+Gz6VxXY7GmF5GF7xiIYQ4NnU4cH/yySeZP38+P/vZz7jmmmta7J8xYwYrV65kwYIF5Obm8umnn/Luu++Sl5fHli1biEQizJ07l/Hjx7c41+/389xzz7Fx40by8vIoLS1l3LhxzJs3r3OvTgghxEHVb6nCYStKkn18NHQAmsOgqGds6mlNgpeypPMZPekvvPfTO0jwKDKWraLX4FMIutyNQTuA3zBIrw0zvKiGghQPNW4He30psZ3hCHPuf4dnvDkkWyF+d7rN+G+Pajz36n+EeXUXoGtgAf4I2PBzTTGyn8mX012dem0RS/FpgU2yR2N0hvzALIQ49nU4cJ8+fToffPABjz/+OBMmTCAzM7Nx39KlS1mxYgV33HEHubm5ALzxxhu88cYbDB48mIEDB7J58+YDtl1VVcW8efNIS0sjNzeXioqKTrwkIYQQ7TX3nvX4U3ycsWUX756ci2e/H6MDTjcLT72BrCW7KfI4KBkyhpPKqtnWM3b7iOgaX6UnUep10cMfpndNkMzaIJ/3TeGSvG38+PoCvkqDkqSB9KsoY9ngEUzcYXDx/3zFkrsyWJ5v8Wp+WixxU9PAAAwdbBuUxvp8i1e3RLhqSPxkWNu2WbQuypdlGreOMRjRMxaYVwYUbt3ilDn1bAy4wFagazhdOrvuMclMlABeiKONlINsP00p1eGUoby8PKZMmcLYsWN56qmn0DSNgoICJk2aRHZ2NgsWLMA0Yx/qJSUlpKSk4HQ6WbhwIbNnzz7giHs4HKayspJevXoBcPbZZzNs2DAZcRdCiEOklKJySy2hqhCf/Gw1m0ttylOTuKjgE0YVbmbOxNuI6i429O6J0uJvojmlFfSqqwegIMlL3xo/UV0nPy2Vl4f1odzbNCI+oLyenPJ6CpLdnLJzJyGXRc/6Uu5c8TZjCndR5fZy1S0/YHVqP1IDddQlJ1Lt88WC9qbOxh4hO/bQbS7YncfQYCXv9RxIXo/eseN1AA1sRU5FEf1qK7ANnbN3bOCXZ18XG7nfx2UwvLaIfyd+QR8vOD/fAokuQIdffBtqAtArGTJSuuzfQAjRutXak+0+dpya0YU9Ofp1Ksc9NzeXqVOnMm/ePF544QVuuOEGZs2aRTQaZdasWY1BO0BGRka723U6nY1BuxBCiMOjekctb975CTU76hu3pStFRk0tvcvKeSvrAgZvKwJgV2oSdV5343HOaJS0en/jc1cgTCAQJb3WTyTZGxe0A+SnehlQ4UdX8NHgfuxMTwXgT2dezO9fXcAD77/GnFf/xtgZ/4s/qoPhjA/aIfZc08Cjg2njqqnjex+8xvCyPfz1zsegPgwuE5yx/HdPNEx5UjJbs/qCDsuHjoSwFSuNs29oKmThCoaoX7QMZ+me+OstfC/2p2nAd6+E397aqfdZCCG6Wqcnp06dOpX333+fOXPmkJ+fz5o1a5gxYwY5Oa0v2CGEEOLI+OiRL+KCdgA0DRsHK3qeBjZETIOQw0FiKNQYuPesraN/ZQ1mww+zmm1z2uodDCipAqDQ64IJ8c3auoalQWGik8pEd9y+hy/+FtM//S+ji/JjgXeaBxxtTD41NUIpibx90hjmjr2ASETFAnZn0+0r4HARMBrSbPZ9CXCZgAY1ocbj1mQOIKey5MDXilrwu5fhkpPhojEH75cQ4jCSVJn26nSyn2mazJw5E8uyWLx4MSNGjOCWW245nH0TQghxGBStLD/o/m39Mnn/1FF8Mm44fo8HrSFQD5smhm03HmdUBxqDdoCzNuzGFY5fcCmrqo6hxcWk+QMtrlPvclPkS+GT7KGxDWY7bkGaBrrGC2Mn8lnPAbFtrQX7tmo5cu8y4u5ybhXFHY3Qpg82tn2MEEIcAYc0SycxMRGn0wnAxIkTMYwTp2xXRUUFoVDTSE5dXR21tU2LjYTDYcrL42+WhYWFB31eVFRE8ykHcg25hlxDrnE4rpE02MuBVPkS2J7dG1vXSamuY8ymnZy3eiO5uwoJOEzChp9rv3yZq758iRJHfFpMWm2A3z7zNv1LqnCFo4wqKOXOD9cxsrCMe97/gpF7SuOOzyktRFOKO79xR2zD/oF2a1QsIDeUTf/q0qZt7aFUU6qM10FQd+A3HQc9BSAyrHfj34/Vf3O5hlyjPdcQx55OTU6F2ESnGTNmsHr1arKzsykoKGDRokUMHDjwgOe0NTl1fzI5VQghDl3RyjLemPYx0Xqrxb4dfXqxdUAfEusDTPgiD73ZLSG/dzo5dau4ZfX7ANx28YPc8Vb8gkz1Hge7+qQQTHATdccHxUEDfnTteQA4ohEGlRezuWdvlK6jRS2MqEXU7TxwxxuCdpRi7tI/k11SwuVf/25spD7RFRf4D6koJL9XL0KOZu3ZNoRtMDRAY8y2Laz+688OPmJ1zWnw4g/gBBqIEuJIW6091e5jx6l7urAnR79O57gvWbKEzz77jLvuuouLLrqIG2+8kZkzZ/LMM8+cUCPvQghxtMscn87kDy6j4MNiqnfVs3VeHqHSIErXSKyLTTzNKimPC9oBMkqrKE1Kbnz+0Kq/8/exV3DmV0UkhCP4XSZ7M3yxna0MnmdV15FTXEZ+agpJwTBlCUn89YU/kVlXzUVbvqQoMYkJ9/2awuQeELJigba3IfBWCsIKnIBt80VaX9Lqarhsz3re6D8KVR8Bjxmr/a4UOxLTcAfD2Aqiuo5CBxvQ9dioe9hi8pcfUzBuGOkpDrylVbE8+UgU7rwEeqVA3zSYMPRwv/1CiDZIOcj261TgXlBQwJw5cxg2bBhTpkzBNE3uuusuZs+ezcKFC5kyZcph7qYQQohD4fQ5GHRZXwDG3nVS4/a/3vIJGWWVLUpAAkR0nfGFO6l2enFbYYaUF3IT/+XpC24gYrhJqPOTVVyGYdnYrZzfv7SUrMoqdqUlU5aUAMB9197O7Z+9gzfBYMzC2xi0qgeFRcRqt0eB+kjs7/u+Q1iw/naTET/8OvB1vgFUBRR76mwWvrCLU+uLuX7aSOiRRNhS1ITAsmz6/T5ARDdio/JhiwRDcd+H0/A4JEAQQhy7Ohy427bdOCl15syZjaUfb7zxRpYtW8a8efM455xzGDRo0GHvrBBCiMNr8pOnknjSEgwF5UkeDLtp1H13Zjo7h3yD6KqXMdP7YI/tw7qVioDXR43LRWlqEnuy0skpLMZQCt2y0C0bTzhM7p49eEIh3vnTYOZv0Ln/rSghNEJOJ7kzr+DcsbGVtz8cDllPBCkKA6YGthYbKdcAh8adozVG9Iz/FTfFo5HiMXh0xiCg6V7jNDTSvQAG4ZmJFNbafJJvc2ofJ/1SZOElIcSxr8OB+6JFi1i7di333XcfgwcPbtyu6zqPPPIIkydPZubMmcyfPx/DMNiyZQvLly8HYN26dQC8/vrrrF27FoArrriCrKysxnYWL17cOJkiGo1SVFTE008/DUBWVhZXXHFF516pEEKIFjw+E6cF/Usq8QYj5GVnEDENIoZBj2CYXT18JM2+iVOvHQDAnge/ZHO+jmXEAuGIabK9VwbpNbX4ggH6V5YxuKiQKk8C9d7Yl4DpYwymjzlwCmXhd91sKbeY93mEekujPAR9fDr3n2bQP7nzAXeWT+e6ERKwC3G069RkyxNUhwL3HTt2MHfuXEaNGsVNN93UYn///v25++67eeKJJ1iwYAFTp04lLy+PuXPnxh33yiuvNP795JNPjgvcn3vuubiZ03v37m08f9y4cRK4CyHEYabOy6TuVT+ZVXVkVtUBsYHvv19xJt5gmFOvHd54bLILQnp8uknI6WBPeg961NZi2LArvSf9S0qouaiu3X0Ykmbw20tlfpQQQhxMp6vKCCGEOD74qyP89ey3GbmjmOS6AEGng09OHsKmAVmM9NXznafHNR5bubWaGT/ajcPlicuLNy2LgaV76FkRZEBRMV+e6iP58jJuu+02HI62SzAKIU5cK7U/tfvY8eruLuzJ0a/TVWWEEEIcH7zJDs78n+G8/5iiNjmJihQfprLpv6eQ6W+dHXdsak4ymZUbMNwRKnyJWLqGw7JJDIUo9qVxyox0Jl6STv7C+Ufo1QghxPFLAnchhBCMu2kAzmQH7z22maTCUjyn9mLay+eiGy2rsLiiURKDQbzRCGHTRFcKVzhCcbKPBNPEI3cWIUQHSDnI9pOPVyGEEACMvKoPI6/q0+ZxiVELdB1nFJzR2KJONhB0mLjdcgMWQoiuItPthRBCdIjTbeCtqcdqyHGP6joFqcmk1NSR2NtzhHsnhBDHLwnchRBCdMik/xuFwwqyPiuDr7J68WWfTMp8iQQcJglSGEYI0UGqA48TnQTuQgghOqTvqBQyrL1YpknIYTZWlylJTaa+NHiEeyeEEMcvCdyFEEJ0iG0rQg5ni+2aUqQPSzoCPRJCiBODBO5CCCE6RNc1Sgfn0KO+Km67x47SK01qtgshOsZGa/fjRCeBuxBCiA67629n0tdr07OmjIRgPRm2n3l/6HekuyWEEMc1KQcphBCiw7xeg0fmn4yKWGDZaG4ZaRdCiK4mgbsQQohO0xwGOKSUjBBCdAcJ3IUQQgghxBEjK6e2n+S4CyGEEEIIcQyQwF0IIYQQQohjgKTKCCGEEEKII0ZWRG0/CdyFEEK027vrgvz5rToSHPDjScnkZEg1GSGE6C4SuAshhGiXJxZX8tDHFkqLTSRb8otK3rjVzZnjZbVUIYToDhK4CyGEaJNtK554px7l9TRuCxsG3/tjKZ/9LYnK6ihz/l3LV3uiXDxKbi1CCNEV5NNVCCFEm0JBm6Desp5BvWbw+rx8XnthK3d/9gm3B/y8NWgIS886nWsnfHoEeiqEONZIOcj2k8BdCCFEm5wuDVtreXP1RiI8+U49C999E13Fpphdu3kDiaEwW7J7dHc3hRDiuCblIIUQQrTpjT/tZFA4wvD6AB7LxmHbKMDvdDBx987GoH2fc3fvQG2SiatCCHE4yYi7EEKINj39qeLkSABXJMooh8HnPZLJS/GxV9co83pbHB9wunCaUuRNCNE2SZVpPwnchRBCHNR7z+zAbduk+P04LJukIPSvrac2X0crraEgO4tatwdfMADEajJ/PjCXQIKMuAshxOEkgbsQQohWvbkuyJxXa7HzbEbsLuGDQVlsTfCSGLU4s7yK/v4Ay3P7Mba6loUTL2bknh14wyE29+pLqS+FpPXF+KsjJKe3HcDbtqKy1mbjrgh9MwwGZErQL4QQ+9OUUvJbphBCiEahkMUdM3ZCUKEr8O0q5aueyXzUv1fjMYatmLprD5+l+Di5tp5ewXB8G6ZJrcuBzwrz2D9PPuj13vm4njl/LadO01FA1AE5g9w8/f00tFYmxAohji8faE+3+9iz1bQu7MnRT0bchRDiBFZda/HqslreeL+e2lqFx6OhlQcJGwa6AdVOk2u2F/HM2Jy48yxdY4vbhR2N8nF6KtcWFDfuCxg6BT4vSVGLoHLy5yVl3HlDeqvXv+23pRRuDLDL48bSwKUUbltRmFfPqx95uPqshC59/UIIcSyRwF0IIU5QeduDPPC/ZaAU7BvZro6SqMAbiVLudRPRNEIOE3ckSp3hjDs/taqGVTnZlCd4qHWYnFpWiXI6eG1Qb0KmgW4rJhaWsefFMm67Lg2n0TR6Xhuwuen7O9mpEqhN8GAqxSB/EJ8Vxa8bRHSd59+u61TgHowq1pYoclI00r0yYi+EOH50aTnIJ598kvHjx/Pyyy+3un/GjBlMmDCBvLw89u7dy/jx4w/6+M9//tOV3RVCiBPK/zxWFvtLQ9Cu2zY9gyHclk3IMADwKMW/Tx7EN1ZvjTs3ORiiONFLeUJsJdX8BA97XA7eGJhFyIyda+saH/ZOZ1uyl/vftuLO/8kThewikaimkWArBgRCZIbDJFo2GZEIJ5ft4dsv/gNq/B16TX9YHcXzhMUZz9v0mhNm8Nxw2ycJIcQxoktH3KdPn84HH3zA448/zoQJE8jMzGzct3TpUlasWMEdd9xBbm4ugUCAn//8562289hjjxEKhTjjjDO6srtCCHFCicbH0ti6TrHXjaVpGEqRHAqjK0V5WiJvp/pwRi0iho4nHMXvcvLe4H6N5+bW1DEiEOJTx363FU2j3ulg8QfVZH64m+V9B7Gsys3QQiceFPvG8EvdLs7Y9QXVSVlUepMp9vXkpJJ8Kkb+kDW9+9NvyzacVpTNGb157+wzKR2WgxaJwqBerI8m4DXh3nFw/7vNXo9hsL1W4fy1n1Oynfz+fJ2JfWT5EiGONlIOsv26fHJqXl4eU6ZMYezYsTz11FNomkZBQQGTJk0iOzubBQsWYJoH/v6wbt06pk6dyoUXXshvfvObruyqEEKcMP71Ti3z/lHd6j7Dtsn0BzEa7g4KWJaSyC63q/EYy9BxWxb1TgeGZfPrVau45sv3GDvjRwT2C96T6vzUed0Y2PzsvX/ygw9e5eELp/B2zvi44/pVlTAoEKIsMbbiamKonoff+CPeYB0p4aaR94DpYPT3fsvW9MzYrwX70nz23c4ONKFVKfonaay6xaA6BM9vtHDpMCpDY3Cyhj8KVSFw6DZlAQ2lFJsrYXymRrYvdp3BKRJgCHG4va/9td3HnqNu78KeHP26PMc9NzeXqVOnMm/ePF544QVuuOEGZs2aRTQaZdasWQcN2gH+9a9/AfD1r3+9q7sqhBDHpdDeerbO+ITKt/ZQNboXfx83gsrwgQNQXyTaGLQDaMCoukBc4N6rup4fLVvFu8P7MHX1l4yu20TvmlJ+/u7r/OiiK4kaOppSJISj1CTGFmiy0Xn4/Bv4z+AxnLm7oMV1gw43ZUZTTnudK4EXx1zMvR88F3ecJxrh+i8/5bfnfx27eZDeVgUaTWNXjSL9iQigQGsYfTdaC/jVfn+PPXcb8O43Nc7oYxz8WkII0QW6ZXLq1KlTef/995kzZw75+fmsWbOGGTNmkJOTc9Dz/H4///3vf8nMzGTChAnd0VUhhDjubPz2Mmo+KCaqaywYNIT6gwTtAHorP8S699tWluDmwavOIikUIqzDv175EIDvfvQGmxN9PHvyREIa1Hld8Q1pGp/0P4mC5Awy6qNEDRMbMICBNeXUJ2bEHV6Y1CvuR3QbeGfIKH5z4XXtfPX70bTYnS+qxWJxjbYD/maCFpy7WBH6npJSlUIcJpIq037dkuxnmiYzZ87EsiwWL17MiBEjuOWWW9o876233sLv93P11Vej65KXKIQQHRUuCVDzQaxUY0FmKvX7B9Kt8LfyS+huZ3xFmX3Bbo3LxZs5g7lg0n1MvvpWfn/aBfTw+wmhgdKa0lf2by8llVKXk0pdo9rQqdQ1dvp6tDhuePGWpteiG+jA0tGnt/kaDkrTaIwTOhEvRGz4eO+hdUEIITqj26LhxMREnA0f/BMnTsQw2v6Z8eWXX0bXda6++uqu7l6HVVRUEAqFGp/X1dVRW1vb+DwcDlNeXh53TmFh4UGfFxUV0XzKgVxDriHXkGsc6jWMRAeaN/Z5mxBoX4WVoGlQ7nLEJYs4LIsh9QF8kWjcsT0CfqZ8sYoVfQawZPg4HrrgGv487kwya2tiB0TtVq/hjFigYjchh63QgW2+ZIJ6U7A/Zs9GrvvircZzVMOXhYy6mna9ju5ytP2byzXkGu29hjj2dMvKqUopZsyYwerVq8nOzqagoIBFixYxcODAA56zfft2vvnNb3LGGWcwZ86cru6iEEIct3bNWs2umWsAeOnCMWwamNnGGZAUipASjrTYvsHjYkVyIhArH7ns+WeYdf5FvJFzUtxx3/xiHSNKSyhJSCQj6Oe5U8exJaNn4/7BJWUonDib3YEsYHAwRFowzOjCrTywbB6magr8P+87iFMLtrMnKZVTvvsbin0pHXgXmlEKmn//MOlQukwvLxTebUiqjBCHyXvaM+0+9jw1tQt7cvTrlhH3JUuW8NlnnzFt2jQee+wxNE1rTJ05kH2136+55pru6KIQQhy3+j8yjpH/uYQ+3xvBA9cncMe1B1/UyBMOtxq0AwwLhEhpGHU/q2AXw8tLKUz0tTiuyuvhrpUreWT5e9y94jPef/LPjNu9i6y6Si7fsoZvrf8Ix37DRgYQ0HVqnA629RzALy65n6DhaNyfGqjnb6ecS2I4yCvPPMrwwvy483XLOmBqzj5mNMogHwz0WZhY6Ni4rAgeA5w66BqYGqQ6YWBSbJtBbFuCCdcOgXW3StAuhDgyunxyakFBAXPmzGHYsGFMmTIF0zS56667mD17NgsXLmTKlCktzolGo7z++uukpqZy3nnndXUXhRDiuNfj0r70uLQvAP2AU8clMv1nxa0eGzRNJq5bx5bMLEqTU+P2acCZVbX4TYOegSAAV2zZxBeZveOOO7m4jHqni8RwiKBhMPucs3DYNsue/gWDK4r4xbnfbjW93NJiCzcpYG9yL/5y6rXc++kLaEBOeTFOK8r06+7gpRGnEtEN0murMCyLU/bs4OJt6/mfy28kbDri2syqKuOhZS/j7uXjG899mx5eHXC0cnUhhDi6dWngbtt248j6zJkzG0s/3njjjSxbtox58+ZxzjnnMGjQoLjz3nvvPSorK7nxxhvbLBcphBCi49JTDzzPKKekmMvXfk5FQiKPX3FdXBqJDSxP9XFSXQAjIZkKTwIPfPIB5R4vz40ei2nbnL27kAzL4F/jJpLsr2fZwH7suXQon96VAE8+AX95m60rEsktzSevZ3bThZXCa9m4ohY6UOEwue7F69CMS+C2P8KGAhIuncDMmWexONNkQ5ki1e0iq7AQHINgwOmsej3Kwg3EtXlLVoDv/PtGSE083G+jEEJ0qy7NcV+4cCGzZ8/mvvvu49Zbb43bt2vXLiZPnszgwYOZP39+3GTV73znO3z88ccsWbLkoHnwQgghOu/RP5fx/spg3La02loeeOt1+lVWENF1ZnxjEhla0wDKugQ3q3wJOG2b27/aQsSpccuaz0irr2F1Vh/W9h1CejA+zabc5eDn75yFoTd9AfhgU5hNN/6FD/uOZkXfXExbkRaJkBqNkhQO4zdMUgZ5+MvMtvPx97d4o8XvVip6J8IfLtDpnyxVyYQ4mi3T5rf72PPVbV3Yk6Nflw1n79ixg7lz5zJq1ChuuummFvv79+/P3XffzRNPPMGCBQuYOjU22aC4uJhPP/2U0aNHS9AuhBBd6IfT00hIqOQ/y5tWJU2p97M7vRf56T15M3cEX6WmkmdbOJRGmdOkrGFV1LCuo9xOeobCXPnNyZR53Xgtm6u255NWWBqXBpMxwBMXtAOcfZKT9AU3Y936PN//aAlvDxzD/51yKV6HG83jQgEffietU6/rW8MMvjWsU6cKIcRRrVuqygghhDh6WZbisy+DfLU5xIghDhbM2UNt2KDOYVKl62x3mZTsV8fdZds8sGErbsviw8x0arxeTqmoxgCUbdOzshpnNAoqxD1vXYTb13pOeV3A5oGfF7K5TFHudKA0DV0pvpYNv/9hr2549UKII01G3NtPAnchhBBxggGbpf8o5aWPgtSjs8rlpHc4wiavG6VpmLbiotIKzigqAeDLtBSyrPhbiR2xuGrFf5m4cRqu9JQ2r/nZmnr+tKCCgK1x09U+rrwouStemhDiKPRuBwL3CyRwl8BdCCFE6wJBm3t+mM8LKoHc+iA9QyGGV9XSs76+MR3mk6wMhgRCLc4don/B1H98C4dDKrgIIQ5MAvf2k5ItQgghDsjj1rn/pjReX+BndUYqmlJEUYyNRgjpOh9k9qR3pGXNd00pBmwtOQI9FkKI45cE7kIIIQ7q5FN9TPhjMf/2elCGzjv9sninXxZYsVVNPaUV6LaNrTdUb1EKw7JI85QewV4LIcTxR2pkCSGEaNOvHu5DUiCIHomiWTZ6JAq2TUowxLYEL7ZSmNEopmXhsCz0SJDKUe4j3W0hxDFAobX7caKTwF0IIUSbnA6dqMNEmTqgsB0GOE0CTpPvv7+S9PIqDMvGiFpogRBnbXuXneOkKowQQhxOErgLIYRo08BMB8qyUZqGMo3G1VQ9lsUdr51DYaILZ3E5qrKcLSlRVnxnMMqQW4wQQhxOkuMuhBCiTU6nzkhCrA7pWA2Bux61ePAiNyn9Epn95hmU1imqg4r+yRbz52840l0WQhwjJAWm/WQ4RAghRLu89qssTkm26eEPkhIIMv0Uk59ekdi4v2eiRk663FaEEKKryIi7EEKIdklP1Pn0kfQj3Q0hhDhhSeAuhBBCCCGOGPtId+AYIr9pCiGEEEIIcQyQwF0IIYQQQohjgATuQgghhBBCHAMkx10IIYQQQhwxSpdykO0lI+5CCCEOq+fX28ytPIfX6kYRtdWR7o4QQhw3ZMRdCCHEYVHhVwz4vyC1EUDrz5r6bBL+N8qO75r0T5FxIiGEOFTySSqEEOKwuPy5ILVRDSwbwjYoULbigr+FjnTXhBBHMaW1/3GikxF3IYQQh2xzhc2KQsBWTUWZG9JktpdJlWYhhDgcZMRdCCHEITvjmQhoGkRbCdINjT21ErwLIcShksBdCCHEISmqU1TUHzwwP2N+pJt6I4Q41ihda/fjRCeBuxBCiEPiNhsqx2gatHZfNXV213Rrl4QQ4rgkgbsQQohD8tHuKI0Ru9OAfaNiGuDQYwE9ihV7rCPUQyGEOD5I4C6EEKLdqoOK//ssyvwvoo3bblhqNY20a1oseHeb4DLBoaNZNmnVAZ69cTl/We7nyj/X8cxnUmlGCCE6SlNKyeoYQgghDshWipoQvLMzyjf+GX/LeP2bcPnzdmxkfV+ae/PbiqHhjkTpU1qPqWxCCkKGgaYUIafGgmvdELQ491QvicmO7ntRQoijxuvuhe0+9vLgzV3Yk6OflIMUQgjRKtu26T0nSnH9gY+5/AUFLiP2RClQNKTGNGvHaZDjD+KzY5F9kcvBJrcLTzDKZ3d+xLDKWuYnJ5CTFuZr/7oIMz2hi16REEIc2yRwF0II0UJFwCbt/5rSYdCJpcNoWixAb0xXbxakx1LZ42g6DCmqwWi2IzMUodTtQFlRItk92NQnhQ0pPh7NSKPuVwHOcJbxz1l98bqNrnlxQghxjOrSHPcnn3yS8ePH8/LLL7e6f8aMGUyYMIG8vDwAlFIsXbqUyZMnc+aZZ3Leeedx33338eWXX3ZlN4UQQuwn7f+isUBc12IPreGhALSmu8e+uH3faDuq4e8Kp2WRvbeGYBQ2eT1sdztRgF/XcYUsRtcHKUlNYXVWBv/s35vBeyuZuKWQj0OJ3HT956z0/onS5bspHT+bD/o8weLhf2PTiF8Q+Muy+HQcIcQxTRlaux8nui7NcY9EItx8880UFhayePFiMjMzG/ctXbqURx99lDvuuIM777wTgP/93//lxRdf5JRTTuH8888nGAzy0ksvUVJSwpw5cxg/fnxXdVUIIU54JfWKX38c5S+rFX67WWWYfX/uX6p9X7DefAhINW3vXVGPJxJfScbWYafXjdI0DFtxdnkVflsx45UVDCqrBqDO5eCRr0/gg8Uz2crJLB+RixY22JHRg5190zm1YCsDK4s5c+FVZI7refjfCCFEt3ot4bl2H3tF/U1d2JOjX5dPTs3Ly2PKlCmMHTuWp556Ck3TKCgoYNKkSWRnZ7NgwQJM02Tz5s1MnjyZiRMnMnv2bLSGHMna2lq+8Y1vkJCQwNKlS9F1KYQjhBD7fL43yoVLFLXh2PNeXlh9i86XZRoGity02Ej5f3fazF1jMyoDZl9oUB+BF/NsIrZiTaHiua8gsi8w12nKU2+et77/3UKp+LrtekMajQ3YioElNejNzglrGrsTXHE58E7L5r7lX/D1FZv2NYqOYlOvFG4r/gefJJyBp96DAj4dN4RqnxcAy9DpX7qX7KEpnPW3c9ETnYfj7RRCHAESuLdfl+e45+bmMnXqVObNm8cLL7zADTfcwKxZs4hGo8yaNQvTjHVh5cqVAFxxxRWNQTuAz+fjnHPO4aWXXuKLL75g7NixXd1lIYToVsvybf64RhGxYdoojatzYgMUv/zE4jcrFHUNWStJDkV9CKL7guHGdJWmhY+KA9Bnrt2Ui75vf0NAvaIInv6i2Si41vg/oKmWiyjtO1cp2H91pf1/tVYN5+uxawUdBt5w7FoKCJg6+09cDRs6N21/hVxKKGAg1WSgAaOLyyhgCL3rq6jEw45+PanxeRsvaVo2O3v2IWnjSt4Zs47i0WM4d3Qi6qMCnENTSfvhqTj6J7XvH2Cff3wIC9/DxiRiJKOCNsYVI4g4PARfysPaUIxeUYvTFcQ7QMfI8GL7LSLb/agkH/o956EZGvYrX6L1Tsb8n4vQh2US/Kqc0t+tRnt/PfUlighufJdk0+cvX8NMdXWsj0KIE1q3TE6dOnUq77//PnPmzCE/P581a9YwY8YMcnJyGo8Jh2PDRW63u8X5+7atX79eAnchxHFl+W7FRUts7IYY+9Vtiheugk0Vip991DRcrYDqSLPZn81Hxfe3b3vz/ZoWC8yh9dVN9+Wuc4AA3T7AeftyThuDdq0hpUZRluQmq9JPwDQo97qwdQ1sBZbd+DJSA/WcVFmEmwiDyWMTbvwkoQEl9CecUc4zp51FZjBAQiQaf2nbZkzhNvpX7qZqzzpWfnwOfUuq8b+TT90r2xiUN6X9I/Hz3oI756LQCdIL2AuA/fYmQrgIExvpN6klMZiP9kXTW6NIxy4MY9+/NO4tsv61Dv3t+9l60aukVu+ilCwsYiUvQy/uJlzyb4a8f337+ifEcczWD/BZJlrolrwT0zSZOXMmlmWxePFiRowYwS233BJ3zMCBA4Gmkfd9lFKsXr0agKKiou7orhBCdJu5XzQF7fs8tVbxh9UHyGLU9j0O041O0axCDC3TYeK2qfiN+n5fDPbRNXBoRLwO8nskUJrgarox6xoYsVtPYijIX/6zAE80EmsCSKE87tJvDjqHbb0zqPR4WnRLUzZZNbH7QkqoFpXYVLcyuqeO2pe3HfBlt/Dkf2Ln4QHiq9k4CTW+di8V8T9IACZ1tPrGVQXwP/w2dnUYk3Bj0L5PzQclREr87e+jEOKE120J44mJiTidsZGPiRMnYhjxH4xnnnkmAwYMYMmSJSxYsIDdu3ezZcsWZs2axbZtsQ/fYDDYXd1tU0VFBaFQ08p/dXV11NbWNj4Ph8OUl8ffgAoLCw/6vKioiOZTDuQacg25xvF/jUAwzP7CkWir8XOjLp2Z1CzFBprluDe/uGr6AhHXLxXL49mX5+6PNozU73egrjG0po4tT/2M6zetjtsVIX6EfHfPVAB2pqWAaj47VnH+9g9wWpHGLa7ofquxWqr9/x7WvrY7/oXooGdYsfdSx2qxS9NBdxnH7H+7co1j/xri2NMtK6cqpZgxYwarV68mOzubgoICFi1a1DjKvk9hYSGPPPJI4wg7wKBBg7j66qt54oknmDx5Mg888EBXd1cIIbrN2zttLllqx8XiCy/X2ValmPlxKx/PdrNKLh0Zdd9Xe33/gFvRerWY/e1/qX2lIpuzVFNKjVKx2a77UmiaMW2bi/ZWct+br3BpwQfoDa8+PzGN3aGReCKxLwfFGQ5+9/UrG88bUrqbWz9/h4DTw8l719EjWN10aU3nw/QL6VUaAMDI8DJoy20YSe3MIf/Da3D/X1HoBMik+bhWGCdBYotCuagild1xp4ZIw8IDqPi3KdGF/sZ32HLJa6TVb6ecTEINKTcAPb/Vn+x/XN6+/glxHHsleVG7j726+sYu7MnRr1ty3JcsWcJnn33GXXfdxUUXXcSNN97IzJkzeeaZZ+JG3rOyspg3bx5FRUXs3buX5ORkBg8ezJIlSwAYMGBAd3RXCCG6zdcG6LxyLfxxjSJswbTRGpOH6SilcBs2/7tCUd0wKO/SFaGGgVvNBqWp1tNmmgfL+0bM9w34KmKZIIrYl4DWzt3fgXLim1/HVo2jy41tKwWhKJgGmHpj+7nVfgzgqTMv4sXgKQwu3UOpx8Uzo8YT0RyM3lNKarCcdZl9yaHpRrWlZz/cVjUn71qPxwpS40wAFDXuJHaffgq543MJvJuPc0gK6T89vf1BO8B3rgCvE23hctzKJGKkoEI2+uUjUA4v1subsPdWY1cb1Ls9eHpZ6F4DO2BDfgQ9wYs+/Ww008B+9Uu0PimYD30NfVw/Bi2/ntL//Zz0z78kUlNBJMFH0s2jSPvVee3vnxBC0A0j7vtKPw4YMID58+djmiYLFy5k9uzZ3HvvvUyZMqXNNr773e/y8ccf8/LLL5OVldWV3RVCiGNKeb3F0GcUFQ2/kCeasP42jfd2KwwNRqZrDE2Ff21TvLRFcekAjTP76BgafLLHJr9W8Y/1io3lDQPvzUs6wsHzQPbdPRoXX2q+T0Ew2lRjsmEhJyNi8bXKWqKahqu+jnN3N81deq9PBq8P6IumFKqh/vt5Bdu4qnA7Aaeb87etokdtgJeGXU7P+nLG7NzIsN9fSuKNY8DlQAhxbJIR9/br0sDdtm2mT5/Ohg0bWLhwIYMHD27cPm3aNPLy8njuuecYNGjQAdtYvnw5Dz74IFdeeSUzZ87sqq4KIcQxLWorQlFIcHZ+0mpJvc0D/7X4e57C3jdC31pzzUfZ9/257wFNq63WhWkx8xY4uS5Avaa4fd2muIlWNvCbccP5yScvceWm7ZQ7Uhjk30RmIJYSU2+6KUjsTUh3UeLN4vy1t2Kk+Tr9eoUQR4eXU59v97HXVE7uwp4c/bo0cN83sn7fffdx6623xu3btWsXkydPZvDgwcyfPx/DMPj5z3+OUoqTTjoJl8vF2rVreeONNzjppJN46qmnSExM7KquCiGEaFAdtEh5fP9a78001nanIXe+WZrMPoYGwUizovPNmDqDamq564vNLXadums5FUYiEd3gXyPH8K1L0zj/9f+S8NrHAJQm9yLxt9/Cd/tZh6+yjhDiiJLAvf26LHDfsWMHN910E0OHDuXpp59uUUUG4LnnnuOJJ57gnnvuYerUqbz44ov885//pKCggGg0St++fbnkkkuYPHlyq/XdhRBCdI0lX0X45iutpMC0Jmy1PG5fSfhAJH67EVvJ1aFsfvrpOjzRpi8IFoqCqM3tvx3N2LPSD/EVCCGOFRK4t1+3VJURQghx7NlZpbhqaYT1pRw4gLfthhKQ+23XiNVrj1ixB8SPkJs6Z+0s4Lw9pSSFo9QbBvXJGo+9dAaa3m2VioUQR4HjNXDPy8tj1qxZvPfee5SXl/Ppp58ybtw4Zs2axTnnnMP555/f4Ta7paqMEEKIY8+AFI0vpzXVVX9hQ5Rv/Su+duTto+Gvq/c7UaepVKTDiI2yR+2GdBpFrzo/p+0p5qvhfbnrO0nUvLWFnLPTcJw9rEtfjxDi6KSOw6y3tWvXcvbZZ+Pz+TjvvPN44YUXGvfV1dUxd+5cCdyFEEJ0nW8ON7lmqOL9fBu3qXFWv9jd9q+rg8QWZWq4+xr7jZjrGjgNsG1e/IbJOzucXH1SGhfnGGiaRp/xmd37QoQQoos99NBDjB49mrfffhun08nixYsb95122mm8+OKLnWpXAnchhBDt5jI1vjYofs7S2rtMTv6rDQ4dgi1XCAVAwafTXEzoY3DdiG7oqBBCHEEfffQRzz33HF6vF8uK/1zs1asXRUVFBzjz4CRwF0IIcUjGZDrADra+mmozE/q0LFIghBDqIJ8bxyqlFE6ns9V9lZWVuFwdWCCuGZkBJIQQ4tDpQETB/usgNZSN1NtVnkYIIY4Po0eP5qWXXmp13xtvvMEpp5zSqXZlxF0IIcQh++NFOve+ZYPdbORM0TDrTPGL82S0XQhx4rj//vuZPHkyCQkJ3HzzzQDk5+fz7rvv8swzz7B06dJOtSvlIIUQQhwW2i+CTU8ay0QonDqEfiprcQghWvdS+t/bfey1ZZO6sCeH169//WtmzpyJZVkopdA0DdM0mTVrFg899FCn2pQRdyGEEIdF3ySNglpiI+374nZLMWm0ZGUKIQ7MPv5S3AH48Y9/zC233MKbb75JcXEx6enpXHLJJfTv37/TbcqIuxBCiMNia4VixNwQ4X13YVuR5tFYM91Jv+Tj9M4shDhkL/Zs/4j79aXHzoh7V5ARdyGEEIdFTg8N/0Mu3t4a5sl/r6GPUcVvp1+AzyNBuxDixJKfn9/mMdnZ2R1uVwJ3IYQQh42ha1w4UCc/cR0AblOCdiHEwR2P5SAHDBiAph38de1f3709JHAXQgghhBDiMHrmmWdaBO5lZWW88sorFBQU8NOf/rRT7UrgLoQQQgghxGE0ZcqUVrc/+OCD3HDDDezevbtT7cpUfyGEEEIIccQorf2P48GUKVN4+umnO3WuBO5CCCGEEEJ0k2g0SlVVVafOlcBdCCFEp31eaHPLa1GeXR9FqgsLIcSBRSIRVq1axSOPPMKYMWM61YbkuAshhOgwpRS+2Rb10djzhRvh1jcsdt6h09t7ZPsmhBBHmq7rB6wqk5qayptvvtmpdiVwF0II0WE3vdoUtDc3+C82gfu7vz9CiGOXaqNs4rHo4YcfbhG4u91uBgwYwOWXX47P5+tUuxK4CyGE6LDnN7e+3QIeWW4xoDs7I4QQR5mZM2d2SbuS4y6EEKLjbPuAu/53jdxahBCiK8iIuxBCiA5zRiKEXa4D7D3+fvYWQnQd+zj5yPj5z3/e7mM1TeNnP/tZh68hgbsQQogOy6yuJD8j80h3QwghjhodSY+RwF0IIUS3WLE3Sn5azyPdDSGEOKrYB0khPFwkcBdCCNEhE58HDOPABxwnP3sLIbqH0uVDo71kBpEQQogOaXNMScHWsIzICyHE4SYj7kIIIQ4vTeP3wUv5k3Phke6JEEIcMe+//z5/+MMf2LhxI4FAIG6fpmls27atw23KiLsQQoj2U4r/fXUhKHXQw2wl40JCiBPXhx9+yIUXXkh1dTUbN24kNzeXPn36kJ+fj2manHPOOZ1qt0sD9yeffJLx48fz8ssvt7p/xowZTJgwgby8PADmzJnD1KlT+drXvsYZZ5zBFVdcwXe/+11WrVrVld0UQgjRTpO+/yUu1fUTsIQQJw6ltf9xrHjkkUe47bbbeOONNwD45S9/yQcffMDq1aupq6vjuuuu61S7XRq4T58+nZycHB5//HGKiori9i1dupQVK1YwdepUcnNzAfjyyy/Jycnh1ltv5aGHHuL6669nz5493Hnnnfz73//uyq4KIYQ4iH9siKL9NsLiXrk8cPWtmLZixK4SetT4j3TXhBDiqLN+/XquvfZaNC32bcOyLABGjx7Nz372sw7VfG9OU6qN3zsPUV5eHlOmTGHs2LE89dRTaJpGQUEBkyZNIjs7mwULFmCaB/5J1e/38/Wvf53ExET++c9/dmVXhRBCtGLyn6v4e00CNNyAcvaWM/uZN8isrgfgtXE5zPzWedh6w1iQUnz73S+57b11pIx0ctrnk+Pae3fxXl55oRyUhscBU+/tSf3iVfS9fAA9LhmGKquFBCdGovuwvYb1O0Kc82Q91bqBWyn+cBbcfk3KYWtfCNF5z/d7od3HTt79zS7syeHj8/l4/fXXOfvss/F4PPzzn//ksssuA2DZsmVceeWV1NfXd7jdLk9CzM3NZerUqcybN48XXniBG264gVmzZhGNRpk1a9ZBg3YAr9dLSkoKFRUVXd1VIYQ4Otk2vPI5rNoGE4bCFac0BtGdVheA5z+A/DJqd4bY9XwVttIx9DrSPVt4fuxZfDgwlwHF5fz9osvjrvfQvz5qDNoBrli9lfeHD+C/YwZhWhbf+HA9J+/dS4bKJ21lJasSd/LVSQPwuzyk1NeyI70PpGQCikggyj8e2oKmJRLNq8L5mw8wdIXTCtOjpgpnWZSCAT1x10UwLIWtwd60FKpTffgCQTzRKJrHpEYZhKuDFHk81CS6KU/zUZTgodzpIGRoqLCNUjpD/CH6hiMsfB1mvxVii8+D0hT9VYRi26AWHR04M83ijQdSWLJB8eZ2C9PUyO0BxXtC7F1Vgx1RbO3lo28wRN8qP7bboFeOF6MuSkaCxmUXJjFg4IFWlhVCNKcO9fPsKJSdnU1xcTEAw4cP57XXXmsM3JcvX05aWlqn2u2W2UNTp07l/fffZ86cOeTn57NmzRpmzJhBTk5Oq8dXVVVh2zYVFRW8/PLLbN++nSuvvLI7uiqEEEefKXNg4fKm53d8Debd3fn2agMw4YewsYAgDnZwJhoGGmDbPj5JPJcHrrm56fj9fpgdkV/SosmR+SW8O7I/v5//FonKIuh28PFJp9CvZjdFWT35Kiu38djsioLYXzSNqMNBeWYq2Ap0Dd228YTDmLZNVUIK/cy9JFUECfpcjWUoE5WFX9MJer0EG7aVuRwsHT2UkGmQGI3iClmUuxyxnXas/0koCrwuytwOhviD9A1HKI26KHI52GzrQOw4G1hebpLyiwCh5vXqTY1Mv81Ze4O83DudCzdWMKguliqkgB27QxjADmDFezXcfkdPzj43qaP/OkKI48B5553He++9xze+8Q3uuOMO7rnnHjZu3IjL5eKtt97iwQcf7FS73RK4m6bJzJkzufnmm1m8eDEjRozglltuafVYv9/PRRdd1Pjc6XRyzTXXdPoFCiHEMW1jQXzQDvD0f+FH18HAXp1rc+F7sXaBHYxrsV7SoOJa3KEwQZcT3bKw91tsaUPfnpy8qzh+W790bMNgc/+eDC8qb9xe4/XFBe0A+T36klpfSWVCatPGhgVYbF2n3uUiKRBAAwrSMumhVcednxAI4gqFCbmcjdvykhMJmQZokB4Is9PdfLRbgaFRo8dueQFDZ2VSAmdX1pIZDFHkdoKuQ0MOKgCGFh+0A0QVRT43K9KSSI5EGVrXlN8fMXT2X5LqxSUVErgLcYKaNWtWY7bIXXfdhd/vZ9GiRWiaxk9/+lN+8pOfdKrdbisHmZiYiNMZ+5CdOHEixgFW3XO5XDz55JP84Q9/4Ec/+hEjR44kHA4TCoW6q6vtUlFREdenuro6amtrG5+Hw2HKy8vjziksLDzo86KiIppPOZBryDXkGnKNyM74ABmIjYDvbUof7Og1wjuaigVEcbRsH/CEowAtgnaA3379TMp8HvxOk3dGDeSPl47n7dGDAOhZHz9ZtTHvvSM0jWjDdQ3LanVVRdOKxj1v/lO7pmiZSrTfU1vTKHI5iByofwf56d5vGiRGrbhtdivLxVZXW9i2Oir/u5JryDVE10pPT2fo0KGNzx944AFWrVrFypUrmTlzJg5H65+9benyyakASilmzJjB6tWryc7OpqCggEWLFjFw4MA2z7UsizvvvJPa2loWLVrUZk68EEIcVwIhyL4TymqatmWlws654OzcBz8rNsPpDwFQwEAq6B8XdlZ5nJzz82kADCiuZGev1BZNmFELNBoD7H2mvL+WU5uPxiubst4pVCT0aNyUFKihxnPwkejEQABDKU7avZUKZyqWo+k6Ctid1Qur2bXX9vCxvHdP0CAtGCGgYgF27ATVat353LoAuxM81JtGbB5BtFmZSw3wOOIDeA1MU+O0nWV8nurjth17cDek4ViaRp0z/viTx3r57oNZB32dQghY2H9Ju4+9edcNXdiTw+ePf/wjN954I6mpLT8/D0W3jLgvWbKEzz77jGnTpvHYY4+haRozZ85sLI1zMIZhcOmll7Jt2zZWr17dDb0VQoijiMcFr/4IxjYMdIwfDK/+uPNBO8QmuD59D/TpQV99JyZBbGIBscKmP18wdcU7DC3ZyzUr15PkD7ZoImoaLYJ2gGfPHIWymwLg3vXFXLfyNfpW7iUhVE9mdRFhfb8BmP2CalcoTM+aCiZsXEvqriC6FkJvGOG20ShNTWkK2m0blOKU4kou3rALVyhKhcskQSlSIlFM28YADCu+9rzDttmZ4KbeNNCUjduyMff1QwO3bfPjkVGykxtO0CHBDf2Lqzmp1s/JVXW8mZlOWcO/Q72hU+JyEm4I3IeP8jB1Ws+2/y2EEMel73znO/Tu3ZtvfetbvPnmmxyucfIuH74uKChgzpw5DBs2jClTpmCaJnfddRezZ89m4cKFTJkypc029v30U1NT08aRQghxHDr9JFj9+1iQ2pnUk9bcflHsYdsM13UiRXUEPt2D65QM8ncGmDs8meCmCgouHYb3RytYPnIAH+dmt1nNxjYMHrvsdKYv+5BxBXvxmx5SrhjF+HAtX5VbZBUXU5CYxPbMHNAgMVDHmVs+w1BRilIyqUtIxm86iRiQ7i4nOyfA2LPc2GaIL4NZuFN9uC7tR2JWAjm9HWhKYUdsXEkOqnbW4zABU/HwhxofbghT64d6CwIK0v1RgrqOrqDKYXLvRS6+cbKTfj5FcoKJP2CzvThKL59GRroDTdP4FWA33HB1TcO2XSi7J6GIjcetA+mN3zt0XcNuGIHXW0nvEUKcODZu3MgzzzzDokWLWLp0KVlZWdx6661MmTKFIUOGdLrdLk2VsW2b6dOns2HDBhYuXMjgwYMbt0+bNo28vDyee+45Bg0aRE1NDR6Pp0XOTyAQ4NZbb2Xnzp28/PLLZGXJz45CCNGd9tZGOeXXFRSlpuCORgg62y5zqEei/Eh7jUcevLrVXM6tG+ooyg8z/twk3J7uSYHcUWnx0DtRspPgVxc4cZoSXAtxNHh2wNJ2H3vLzm90YU8OP9u2eeONN/jb3/7Gq6++SjgcZuLEiUydOpXbbrutw+11aeC+cOFCZs+ezX333cett94at2/Xrl1MnjyZwYMHM3/+fD744AN+/etfc8EFF9C3b18SEhLYu3cvr7/+OsXFxdxxxx3ceeedXdVVIYQQB6GU4vZ71pKwey9/PO+S9pzAn5Pnc9ttt3V6EpYQ4sRwPAfuzVVVVfH888/z6KOPsnfvXqLRaNsn7afLhjl27NjB3LlzGTVqFDfddFOL/f379+fuu+/miSeeYMGCBVx88cWcddZZrFy5kv/85z8Eg0FSUlIYPnw4P/rRjzjrrLO6qqtCCCHaoGkaz/xpLP/dMYqXni5lT+rB87d12z7ofiGEOJHU1NTwwgsvsHDhQgoKCvB6vZ1qp1uqygghhDh+aI9FGuuuH5AV5c+pz8qIuxCiTQsGtn/E/dYdx9aI+zvvvMP8+fN56aWXCAQCTJgwgalTp/Ltb38bn8/X4faktqIQQoiOaSNmT62p556kN7unL0IIcRR65JFHWLBgAbt376ZXr17ce++93HbbbeTm5rZ98kFI4C6EEKJDnFaUsHngUfSRu0vJPqOq+zokhBBHmUcffZQrr7ySP/7xj1x22WUHXHi0oyRwF0II0SEPTTT4+Qp1wNKQX/VJ7+YeCSHE0WXPnj2kpx/+z8JuWYBJCCHE8WPWOU60UOSA+6u9zm7sjRDiWGdrWrsfx4quCNpBAnchhBCd4AsfOHB32R0vcSaEEKJtErgLIYTosDrvgRdhOmNHXjf2RAghThwSuAshhOiwA1ZpV4qHghK4CyHaT2ntf5zoJHAXQgjRYQlm63dQRzTMuXOv7+beCCHEiUECdyGEEB22+jYD9l+/TynmX+0Ct0xOFUKIfQKBAHv27CEaPfT5PxK4CyGE6LChaSavXL9v1D0WwM/7msaNI6TKsBCiY5SmtftxLFm2bBlnnHEGPp+P/v37s27dOgBmzJjBP//5z061KYG7EEKITrlqkIn6von6vgP1fZM7TpagXQghAN59910uvvhigsEg3//+97HtpplB6enp/O1vf+tUuxK4CyGEEEIIcRg9/PDDXH755axZs4Zf/vKXcfvGjBnD2rVrO9WuDI8IIYQQQghxGK1Zs4YlS5YAoO2X4tOzZ09KSko61a4E7kIIIYQQ4og51nLX28M0TSKR1heqKykpwefzdapdSZURQgghhBDiMDr11FNZuHBhq/uWLl3KGWec0al2ZcRdCCFEp/gjClMHp3H8jZYJIcSheOihh7jkkku49tprueWWW9A0jRUrVvDMM8+wdOlSli1b1ql2JXAXQgjRITuqbIY+bdO8IvEnk+D0PnJLEUJ03PG4IupFF13EggUL+O53v8vLL78MxMpApqSk8Le//Y2zzjqrU+3Kp6wQQogOGfy0zX5LL3HG38F+ULWYhCWEECcay7LYtm0bV155Jddffz0ff/wxxcXFpKenc+aZZ5KQkNDptiVwF0II0W7BqGoRtO/zxEqL750qtxUhxIlNKcXw4cN59dVXueyyy7jwwgsPW9syOVUIIUS7haP2Afc9sLwbOyKEEEcp0zTJzMyMW3TpcJHAXQghRLt9sOdA4+1CCNE5Stfa/ThWfPvb3+bZZ5897O3Kb5pCCCHa7RuLQ+B0HXB/KKpkREgIccI7+eSTWbx4MRdccAHXXXcdWVlZLeYAXXfddR1uVwJ3IYQQ7RZ0OA+631YSuAshxC233ALAnj17eO+991rs1zQNy7I63K4E7kIIIQ4PpVAKOHZ+zRZCHAWOx5VTO1unvS0SuAshhDhsSv02vTtf6UwIIY4L5557bpe0K79oCiGEOGy8jtif4d1utEU9eOMnXxKqix78JCGEEO0iI+5CCCHaZfZKC03ZKM044DGVtRbLrnuNEH0JGAa1H1ZRdca/uGjJxWTlJnVjb4UQx4pjqVpMe11wwQUH3a9pGu+8806H2+3SEfcnn3yS8ePHNy71ur8ZM2YwYcIE8vLyWt0/e/Zsxo8fz8SJE7uym0IIIdrhu+8plHaQ24amcevMAnbpPbCNWHBvGQZFyeksveVjSrfVdVNPhRDiyLJtG6VU3KO0tJQPP/yQzZs3o1TnSut26Yj79OnT+eCDD3j88ceZMGECmZmZjfuWLl3KihUruOOOO8jNzW1x7ubNm3n++efxer1Eo/IzqxBCHElvbGv4HG5jElm5z9fyGE0j6HTz7B2rePDdrsn7FEKIo0lrlWQgFt9ec801PPLII51qt0tH3B0OBzNnziQUCjFr1qzGbxcFBQXMnj2bk046idtvv73FebZt88tf/pKJEye2GtQLIYToXte2/sNpnMRgmAs37259p6bhrvWT73uYNVf9g9fOWcKye9/DCrddDi1cFe706JRSihUr61i8vI5lm8NYtiwgJYQ4coYOHcoPfvAD/ud//qdT53d5jntubi5Tp05l3rx5vPDCC9xwww3MmjWLaDTKrFmzMM2WXfjHP/7B9u3b+c1vftPpbyRCCCGaKayANTtgWF/YWggZyZCdDpP+D0qqoU8qlNVh7y5lbY/evNujP3kZffkysz9JdTWkZPWnKDX9gM27w1F+/NZKzIME2L1qK0mpqyf5358TTu9D8d56PnrpK3oYfnoN85KcZmCfO5KSz6vwP/cVVthAUwYainK3l4JUH75IlJQ6P7oOZpJJVcQg4PHQQ0XQPA5qbx7N6940NueHcddGyDddRG2bkTV1+CyLx70eNiT78ESjJHp1Ag4H/Rwh1rsSqVQGOQmK4VuK0atCBFNc7B2aTnVA0deKcP85Li45P5nSesXKPTbKttlVreifZnDhAB2Xefzl6QrRLY7DcpAHM2DAANavX9+pc7tlcurUqVN5//33mTNnDvn5+axZs4YZM2aQk5PT4tiioiL+9Kc/MW3aNLKysrqje0IIcXyb8xo88DeIHmR0e80OIPYzbL+qKH++bDpbezZ8BivV5o01vT5w0KB9YNlevrZpFRAr8z64spA1/YfgsG1O2r2b5N1fYGNS/fcCTEw8OAjiABQhzSRqm/SoDOMmgtJ1okojXKywdBNnVZg6BW+P68uT5mBCmHgzo9xQtIMCn4NtvgR2J3g4q7ScseWVZIXCvJ7Th6+SvADkWTZpwRA1PheVBZUM2lmOw1ZQUUvPwlqeHjqALw03b79uc80/dvBqVgbhkA37Ru816NnTyTs3OxmVIcXahBAH9+KLL9K7d+9OndstgbtpmsycOZObb76ZxYsXM2LEiMYVpfb36KOPkpWVxU033dQdXRNCiONbcRU8uODgQft+fn/OVU1BO7QZtGtKcd6WPa3uc0ZDaMCEnRvi1mVyWjZX5L3PP8ZeyaCyvXhq0wnhxcZAA6INt6eoplPgTkGhkRWsxlR2YxshTAKxHoAGuzKTCTlj5527o5gNPi+rUmOVbPwY/LtPJqmRKJUJbvY0BO0AYUOn3jTRgYvzi2JBe4PMQIhxZVV80iuNqK7zqdPLwNJaNiU2nY+C0vIoD/zX4O3JErgLIWKD1vsLhUKsW7eODRs28Nhjj3Wq3W77hElMTMTpjC2VPXHiRAyjZTmxt956i48++ogf//jHrabQHE0qKioIhUKNz+vq6qitrW18Hg6HKS8vjzunsLDwoM+Liori8jjlGnINuYZc45CvsT4fIh2b4P9VZr92H+sLhLjrwy8ZXRjfR5Ti/G3LuXfFPO799M8kBetbnJteX4nDirCtZx9AR2FiN9yWNGL9rzVcKE3HbUfignYAF1EM1fSFZOTO0sa/D6ysIy+x5UpQG5J8FPo8LbbXuByYtk2PYKjFvrRwuPHvlU4Hmf5gyzfCslldHOvzEf83l2vINdp5jaOF0rV2P44V7777LsuWLYt7rF69mr59+7Jw4UIefPDBTrWrqc7O+OkApRQzZsxg9erVZGdnU1BQwKJFixg4cGDjMTU1Ndxwww2ceeaZPPzww43bp0+fzvr16/n444+7uptCCHH8Ka+FvndAMNz2sQ1+d+5V/ODKm9t1rDMS5advfo7big+qsyt38a2v/tX43E9PoiTGn6uV8NQZU5iwYyOjCzcRxEcEJxpgoePHS6WZQJnTh9sK0zPUspxksZ6E1VCicsk5w3jm8rEA3Lp6G//pkUqJ2xl3/FmlFbh1jf8O7Ru3PSkUoSbByQ1rtzG6pDJu36LB/chL9gEwsqqGoMPB1oT9gn9T5/LRLl77luPgb5gQooU/jf53u4+9e92VXdiTo1+3jLgvWbKEzz77jGnTpvHYY4+haRozZ87EsppGSv7yl7/g9/u54YYb2Lt3b+MjHI5VE9i7dy8lJSXd0V0hhDh+pPngqTvA0xDAtmPA6r4P/8O1X66I5bZD05+tCDtMHv3aeOodsV9J9x2ZXV3AewPOYk3maMK6Aw/lmNQDCg0LN2V80TsXdzhC76oSvBSSRAEmEfy6Ew2FGz+mHQYUQd1BZL8a8iGMxqBdEZsgqzd8gXh3QC8mVFbH9d1lWVhoZNcFyKzxN+5zRS0sQwPL5qPMNLYlx75ghHWd9/r0JC8p9nxgnZ8LXCGKeiVC85E/XaNfL5P/u+jo/qVYCNF9nn322QP+wlFRUcGzzz7bqXa7fMS9oKCASZMmMWDAAObPn49pmixcuJDZs2dz7733MmXKFAAefPBBli9fftC2+vfvz4svvtiV3RVCiONTZR1s2A25fWBrEfRMguye8Nt/QVUdJLhi29ftoj4rnZ3FYTa7k5l7+sWE60K8lzMKDVB66+M9rkiUQeU1XPbldjL98ekmPetKuemLxdSbSTgjNhFDZ21mLl9kjiSjpoyxvUJkjUrC6TXRzs+lbEMNeX/YSLjOpt7rxVkfwYgqNNvGZ4XxRkKYvROIZvio3h3ETnTRIzGKM9lF3f0TeMVIpWpdFcbWKnbjZkuCh3pdp28gSEYoRF6PZAKaTqpP4UhycqkvxId+F1ENLh7ppPqzcrYUWwzo48B5UjI11TaDVJibLvaRme2hNqRYV2Tj1CG/WtE7RWNCHx39BKuMIcThcjyOuBuGwSeffMJpp53WYt+qVas47bTT4gaw26tLA3fbtpk+fTobNmxg4cKFDB48uHH7tGnTyMvL47nnnmPQoEGsX7+esrKyFm089dRT5Ofn8+ijj+L1elt9A4QQQnStCQujRFdtZ3XfQQc8pldNPQ8uW9vqvou3/JcyZ0+G+mpJvvMsdr2aT+WQ/pzyq9NJSHW2es4+ylZoh5DbWlAYYkdBmJOGeMhIkVFxIY42T415rd3H3vPFFV3Yk8NH13U+/fTTVuPWjz76iPPPP59wuP0pjPt06SfYokWLWLt2Lffdd19j0A6xF/PII48wefJkZs6cyfz58xk5cmSrbTz//PMUFBRw3nnndWVXhRBCHMTySTqPLficNb0HHHDUPWjo1Jk6iVG7xb73s8+kZ7rOha9fDMCo77T/2ocStAP0zXLRN8t1SG0IIURb8vPz2blzZ+PzNWvWEAzGT2YPBALMmzeP7OzsTl2jywL3HTt2MHfuXEaNGtVqacf+/ftz991388QTT7BgwYJWy+YIIYQ4OrhNnWJfMqfs2cHKfoNbPaY6wcPynHSuWb+bqNksUFaKoOHg269f2E29FUKI7jd//nxmzZqFpmlomsY999zT4ph9iS6zZ8/u1DW6paqMEEKIY9/cj/1s/fUb/P68qw94jP87Gi/MWE3Jxvq4+u/T/nEqqX28BzxPCHHievLk/7T72BlrL+vCnhyajRs3smHDBpRSfPOb3+TXv/41Q4YMiTvG5XIxcuRIBgwY0KlrSOAuhBCi3bTfhOhTW0WRLwVL11sszlR8t0GqM8q8JxYS+KQXIyYM5OIHcjEcsjCREKJ1x0vg3tyCBQu48sorSUtLO6ztyiwdIYQQ7adp7Ek58I3IbcaqJDiSozgu3cNFt10sQbsQ4oRz6623dkm7ErgLIYRov4OVPFQKQ9NpquYuhBBtO5ZWRO2IiooKnn/+eTZu3EggEIjbp2kaf/3rXzvcpgTuQggh2k+pgwbvCU6DSKRlVRkhhDiR5Ofnc+qpp+L3+/H7/aSnp1NRUYFlWaSmppKcnNypduX3SyGEEO2m2wcPyuvCMtouhBAPPfQQI0aMoLi4GKUU//nPf6ivr2fOnDm43W5ee639teubk8BdCCFEu9011oyNuh+AJXG7EELwySefcPfdd+N2u4FYGUin08mMGTO4/fbb+cEPftCpdiVwF0II0W7TRnPAVJmBZUUku47PXFUhRNdRmtbux7GiuLiYrKwsdF3HMAxqamoa95177rl8+OGHnWpXAnchhBDtNiTNaH2HUvzqutTu7YwQQhylevXqRUVFBQADBgxg5cqVjft27tyJaXZumqlMThVCCNFuiU4Nnwm10f12aBqTJiQekT4JIcTR5vTTT2fNmjVcffXVXHfddfz85z8nFArhdDr57W9/ywUXXNCpdiVwF0II0SHl9xn0/ZNFSbBp28obj52fsIUQR5nj8OPj+9//Pjt37gTg4YcfZuPGjTzyyCMopTjnnHOYPXt2p9qVwF0IIUSHOAyN4ntjtw+lFNoxlHcqhBDd4ZRTTuGUU04BICEhgVdeeYWamho0TcPn83W6XQnchRBCdJoE7UII0T5JSUmH3IZMThVCCCGEEEfM8VhVBiAvL49JkyaRlZWF0+lk9erVAMyaNYtly5Z1qk0J3IUQQgghhDiM1q5dy6mnnsry5cs577zzsCyrcV9dXR1z587tVLsSuAshhBBCCHEYPfTQQ4wePZqtW7eycOFCVLOF60477TQ+//zzTrUrOe5CCCGEEEIcRh999BHPPfccXq83brQdYjXei4qKOtWuBO5CCCE65fO3S/nn7F2ENYOw0+SmH/VnwvhDn3wlhDixKP3Yyl1vD6UUTqez1X2VlZW4XK5OtSupMkIIITps+8Y6XvntdsoSfZQnJ1Hr8TLv90UseL36SHdNCCGOuNGjR/PSSy+1uu+NN95oLBXZUTLiLoQQosOeeWQLdR43kWbLdluGwZuLSpj8tQFHrmNCCHEUuP/++5k8eTIJCQncfPPNAOTn5/Puu+/yzDPPsHTp0k61K4G7EEKIDtPLA4RSU1psd9qKvEKr5QlCCHEAx1qZx/b41re+xbZt25g5cyZ/+MMfALj++usxTZNZs2Zx1VVXdapdCdyFEEJ0mNMfxusN/T979x0eVbX1cfx7pqSRQhJaaKEECFVUFEW6ih0rKkWIEblgxPbaro3ELiqCiAIqkeoFsYBX7CB4LSACYiECUgOEFtL7zHn/GDIQMoH0kPD7PM88ZPbZ5+w1QzKzZs86+5DtU7ROM98waBhU996ERUTK6rHHHuO2227jq6++Yv/+/TRo0IDLLruM8PDwch9TibuIiJRZpr8vuRYLORYLPk4nANlWC6ledgLsStxF5Mzz8MMPc88999C8eXN3W7NmzbjjjjsqbQydnCoiImWW6uvDrqBAAtPS8E7PYL/NRrK3NxbA6TBPub+ISF3z6quvsnfvXvd9h8OB3W53XzG1MmjGXUREyuSvX9JI9bbTa/subEcvKtL6SArrmjWhwGJjycfJRfpn5Jl8uKmAYG+4poMNow7Ws4pI+dWVGvfjL7J0sraKUOIuIiJl8kXMaloFBXA4MIBcm436WVkEZufQ8cBhfm7Vgh0z/sZviDdvHRnAIw8n45+Xh29BPpk2O1FWO2+PDubGbuVbw1hE5EymxF1ERMrE/0gG69u1Js9uB2B/cBAtDh6mcWoa7Q4e5mBIEHMOXkuK3Rv8TVIKd3SaYJrcND+HH9ft4MKoDjX1EEREaiUl7iIiUibbW4S5k/ZCe0Pq0ygtjRZp6WxuEEKKzQtOrHW3GOBjA4sXvXa0YNviv2l9k5J3kTNdXSmVAfj777+xHb2+hcPhWho3ISHBY99zzjmnzMdX4i4iIqU2e85+Mvz9irU7LBYcVisWINt6kjdhE3cC/+yXWbx7U5WFKiJS7aKiooq1FV6AqZBpmhiG4U7sy6LMifu0adOIj4/nySef5Nprry22PSYmhrVr1zJ79mwiIyP5+eefWb58OQkJCWzZsoX8/HymT59Ojx49iu27d+9eBg8e7HFcf39/vvvuu7KGKyIileTbpQfZFr8Ve1AAHD3hyi8vDxMDu6Pg2DJlFhtYLOB0upJ0cJfJkO8AmwGGwZdBjShIz8UWoHp3Ean94uPjq3yMMifuY8aM4fvvv2fSpEn07NmTJk2auLctXryY1atXc+eddxIZGQnAF198wRdffEHbtm1p3bo1mzdvPuUYAwYMYMCAAUXa7Cd8LSsiIlXPmZXP1xN+5YWM5qT72DmrSSgNsnJpkpZOSHoGvvn5Rzua+GTlsTMslH0B9VxJu7cVCr8CN03Iyne15zmgnp1vZj/PzrfzGXFLDN5WeLhVOv3v74Wfl1YqFjmT1JVSmVGjRlX5GGVO3O12O7GxsURFRREXF8ebb76JYRgkJiYyZcoUOnToUGSh+bvuuovHHnsMLy8v5s6dW6rEPSIigiuvvLKsoYmI1DqpuSYBXmA5xRuXWeAkNz2f/PQ8rN4WjBA/fE+40JEjIw9yC3Cm5GJ6WSjYmY6zbTC2hn5kJKSSnZRJWK+GZFrtBHkbmKbJoa1p+B/JZEP9ENbucvDLPpNB3b3pfCCRQNPgk7gtfN+mFZFZqdTPdSXpJhCakYFPYdIOYDFodDCdJtsP8/6oFmCzHEvawfWz37EJGN+cbLIMG+ek7ePHt+MosFiwO5389PoSnux7M4fqBTD216+4c8MqHM1aUNCqKfa4a7B1C4PD6WAakJuH0SAQM8Qfc8d+jJBAjCb1cWw+gHkkk+wWIfg2CcSam4/zSBb5Pydi69gQW8fGx+LKyAZvO9iLvh0603Ix6tkxjmRAZg40CgJfb8zUbEg8iBEWDCEBmBm54GXF8Cr+dmpm5oHNguF9irfatCyo5w1WK6bTCek5GEHFy5FERMpV4x4ZGUl0dDQzZ85k0aJFDBkyhLi4OAoKCoiLi3MX5QM0atSoXIHl5ubidDrx9fUt1/4iIqezjQdNRn3uYMMBaB4Arw+0cH07zzPNGe+s57mPU+m6ZTd9t+ygwGLw8bld2RR7JW9cZsNyJIc9o74kY9kOwMROPg6rwYTrL+GTcxsCeVz3yw4e+2Ql2eSy6IIuzL6mN9np+ewJDsJW4EOB1Qq4ZsjnfmHSLDWYO9ckcKBBQ5plpBCQfyzpNgBbQfHazPRAHzruTmb8F7/y2LABxbYfL9vHl6uHPcz6tx+ncVYa9qNXX71w7z889b9P6Dfi34y7PJqvWp/LjE8+w7LnCNmXvEc99uFDOubROFwFO1ZM/CjASj71MDApwE4mvmxuYqd70i4AnPhxhCCMQB8abLkHRr4OX/0Ggb7w2I3w8PUUbE0m5baPyf85EYuPQUDOTvw4gomFvHqNsWcewkI+DixkNowk/5CJ4e+Fz0N98HtyIABmei4Zd3xI3od/go8Nn3t64ff8oOLr12/dB7dNgZ83Q+P6mDdehPnJH7A3BfPcVhhzRmN0anaK3yQROZOU+/vI6OhoIiMjmTp1Kq+++irr169nzJgxREREVDio+fPn07t3b/r06cNll13GpEmTyMzMrPBxRUROB6ZpctNSV9IOkJgOt/7Xyb6M4hfqyNu4nwWvb8UvNYu+W3YAYHOaDPllI3lv/8qra032jV9xNGkHMMjHi5n9zueDnl3Jt1nJt1n54MIuzOvdnVynNzev2chu04s9wUEAFNisR3c13P/uC6zHYV9vfmzVlAaZWcXjshWf96mXngPA2dv3g+ks+QkwwLAZ7AtryDVRD5PkH1Rkc5/EBEav/wmAJe26ss8/4OgWC1k0wcTAOHYoDBxAAQX4YcHEAOzkE0g6ZyftOtoHfMjCl0xIyyWn81Pw5QZXCU9qFjwyF775jZRhH5L/cyIAzhyTVFpQgDcGTuyZB7Dg+pYhm8bkH3SCaWKm55L91DfkfboJgKzHviTvgz9cdf1Z+eS8uJK8Bb8Vfx6GveZK2gH2p8Cbn8Hew677v+7AvPmtkp9DETkjlTtxt9lsxMbG4nA4WLhwIZ07d2bkyJEVC8ZioUePHowbN45XXnmFCRMmcNZZZ7FgwQLuvPNOsrOzK3R8EZHTwdYU2HKkaFueA77dVTxxz/liGys6tOGio0n78S7aupNl25xkfF5826rI1sXavo8MBwy2NmhI7gmlIZwwGey0GKxv1oDGWTlctG29u90EHIbBEf965BUm/EC2l518w3XfsJhE/fj7sRNSwfUz0DAzC+wWTIvrBNVfwtsx5sYxRcY2sTHu1x+PxmHhoF+947ZZceBV7LE5sZ74ELBgcuIzasf14cJ5qPj7ifPDX8j/Ze8JrQa5BBz96di3DHnU40R5y/52/ft58ZLQwm1uh9Phl60njARQcKzhzz2YOw8VO5ZIXWMaRqlvZ7oKnQHk7++Pl5frBbRXr15YrdZT7HFyTZo0Yfr06QwdOpR+/fpxzTXXMHHiRMaOHcvmzZt5//33K3T8ypScnExubq77fkZGBunp6e77eXl5HD58uMg++/btO+n9pKSkIpfG1RgaQ2PUzTEa+Zr4eihUbB1kFBuDFv60TE7hn0ahxfr/0yiUNkEGRvPi9dDNk1OLtTVLTgMgOCsT4xSX4bY7HDTMyGZPgB+Zdj+ybVacQL7VisNqxWoYHA4MZE9IfXY3CGVPg1BS6vtTYDU4GOrHhVsT8c3Lh9wCOJIFqdmQ76Cdh7g+izzHnWCbQAH+2JyuJDk85Qjd9ie5+xo4sJJX7BhGsRQdDy3gOFohanh4/o3IJhDsU6z92HjGcW35xfu1DgGgoGnxpN7SKtj9c3JyMrneFgjx9xDhcW/LAT7Q0PWh4XT53dUYdWsMqX0M0zzFq3cJTNMkJiaGdevW0bJlSxITE5k/fz6tWxef5Sk0d+5cpkyZUuJykCUpKCigT58+dOzYkVmzZpUnXBGR08qLq538+/tj5STXtzP46Nrikx9mvoOEyz7g7lbdmbTov4RmumaK/2kYwugHRvDpnYE0W7OD3dd9ipnnOp6Bk12NAhl61y2k1HOdJxSckcWcaR/T7tBBMv2t9HtkDBk+xy3DWFg0ftR5u/Zz1abdvHFRJ678ex+G1Ua93Dy8T1h3uMBikHN01a+uv/+Df1YmDsNgS8Ngpl7Sg2w/H3C4VpLxz8/nqu2JLDy36EWXmqam8M/zj2DFiQNvwMoT/S7nl2ZNmfTVJ7Q65DganBN/9uFNepH9TcBJAHn4Aceewxy88ScF10cOcGIhjQY4sBH0Ui+8npgN+UdnuLuGww/Pk7ngT9LGfebO+r1IJ4RtuEqQgrCTggHk4Uc6LdxPmqVDA4J+Gocl2Jf8H3eSNigeMl0Jv6VVMEE/jcXSJIAiZnwJ42a6v5UwQ+tjHna6j2lMuhXj/suK/U6I1DWv9P6u1H0f/F//KoujNih34r5o0SL3bPgll1zC8OHDiYiIYNasWSXOvJc3cQcYPHgwNpuNjz76qDzhioicdn7ZZ/LdbpPODeDy1kaJK8uY+Q4OfbKVJX878D2SgdnIn8yB7bipi41QX9c+edtTSft4K45dqVgOp5G3LZ2Dpp1vrzkXW3gQ5775P+rvPMSBPuFsjO7NReFWkj7Ywuo/s7Ck5HDQ6s1Of3/WRLTA2xfq52ZhT3MQmppHnpeVFhkFhGTn4uUsWrvuMAyyrRZCUjOwOZ0Y+QXM7tqW3cGBR4N3LRNp+nuTb7fy72/X8N6FndlT/2j5iWkSsfcgzY8c4r6fvuOcPTv4sm0H8qx5XLbrT7LtXmTafem2Zx/5hj9e13TEnnIQ/jmIUZAPHZpintMB1u3EDG+Iw+5F3pIEcvNhw8BOZN59GZd9toqCVTvI2ZmD0SKEwEW3YItoANuSYMkv0CgQbrwQfFzfIOf/vp/cL//B1iIA74wDGD8lQOeWODq0wrnsd6x/bsXo2Rrn7YPI+3wrllA/vG7sjOF3rITHuTeN3A//wPD3xvumLhglrVX/+074cj20bYJ51bnw5Z+QsA8u7ohxTqsK/HaJ1B4v91lZ6r4Pfd+vCiM5/ZUrcU9MTGTo0KG0atWK+Ph4bDabOym/++67PV41CsqfuOfk5NCvXz+6devG22+/XdZwRUSkAobf8icNMnJx+HjjdcJbRuS2nWTb7FhMDih7lQAAc5BJREFUE6dhcCi0Pn81bsBHrY5d48M/O4+A3ALy7FZa5mRy84Yt/N6sAak+XlgLnPzSLJhRa75h+B8/sb1jZ7pFdaNlRDBGXh70igR/rS4mUpcpcS+9Mi8H6XQ63SelxsbGupd+HD58OCtWrGDmzJn07duXNm3alDmYQ4cO0aBBg2Ltb731Fg6Hg759+5b5mCIiUjHzF3bm7Y+P8PX7e8n286Xj4XQsmETu2E2W1YblaDJvMU0aHjpCuK8PITl5JBfOYNsskAte+Q4SQoJ58eLzaHfwCFkOJ2MzN7NouD/ez94C3iPoUpMPVETkNFfmxH3+/Pls2LCB8ePH07ZtW3e7xWJhwoQJDBs2jNjYWOLj47FarWzZsoWVK12fpDZu3AjAsmXL2LBhAwBXXXUVYWFhALz44oscPnyY888/n8aNG5Oens4PP/zAunXr6NatGzfffHNFH6+IiJTDndcHc+f1wcTG/EGfT9fQMjmVNF8ffuzYvkg/A/DJzSuy8oG94Fh5ja3AQWqgLxsahnDBP5sYP//S6nkAInLaMi1aLaa0ypS4b9++nenTp9O1a1dGjBhRbHt4eDjjxo1j8uTJzJ49m+joaBISEpg+fXqRfkuXLnX/3L17d3fi3qdPH5YtW8Ynn3xCamoqdrud8PBwxo8fz9ChQ90r2IiISM14ampn3vx+Gy2TU/HLzcPqcOA44bym/fV8OXR0tt0r30G93GNLHOZZDQb8vonvm7dkZC+VwIiIlEW5T04VEZEz0wu9VtD0YDIN09PZ1rgBDpvdPcOeFBzE5iYNWN4kiCxfb+plFuBV4MQEMv28ICubdG8vojL/Jv7NC2ryYYjIaWJiv1Wl7vvwyjO7bLrMpTIiInJmszhNFl14DgmhgWwPqUdQdi6tk9NI8/WmXUYe7Xbtw7+9H82Tkvm7STPyDavreqY5+Zje3oR5OZn5Ys+afhgiIrVOhS7AJCIiZ56Uxv4UWC3sCfLFNAxS/HxY37wR/4QGkZlfQIMmdv7d+DNiuq8k+WFvHjrLgX9+LkEUMOFiL7bHBWO3qqZVRFx05dTSU+IuIiJlErfwXELSM8m1Fn8LaXzoCA9M6YDFcFVh2q3w4mB/0p4O4shzwcRe7oe3TW++IiLlocRdRETKxMvHitNqJTw5vdi2i/7Zg1WJuYhIlVDiLiIiZRaamsGzH37HOdv3AhCQncuDn/1I5L7DoKXdRKQMVCpTejo5VUREyiwoN4/AzHxmzlpGjpcVrwIHFifsrh+Mza45IRGRqqBXVxERKbPU0EAOBAeSgx1LnkGB00YOdpID69V0aCIidZYSdxERKbPLRjTht87hZHt74cSCEwu5dhsHWtav6dBEROoslcqIiEiZDb42hF9+zeID2/m03XUAw+Fkb0g9Xlp0Vk2HJiK1jGrXS0+Ju4iIlJlhGDz7TAvSMhz8tKElbZvZiWjtDUB+fn4NRyciUjcpcRcRkXIL9LdyWW//mg5DROSMoMRdRERERGqMSmVKTyenioiIiIjUAkrcRURERERqAZXKiIiIiEiNUalM6WnGXUREKlVKch4Lfu/LS/9cy9lTstmT5qjpkERE6gTNuIuISKXZnZDKlW/n8Ud4BJgmYBDxeg4pj/jgbbfWdHgiIrWaZtxFRKTS3PtqEn80CITkbNifBQezyMHKgHeyazo0EZFaTzPuIiJSaX5u0gRS86DA6WpwmJCSy+p8zbaLiGeqcS89Je4iIlJpkmx2vLLz6JCeiY/DyT9+viR72cFZ05GJiNR+StxFRKRShD98EO8Af27Zd5Cg3HwAzk9JZ1mjENIKDEBXWBURqQgl7iIiUmHbE3PYVT+Qcw+mupN2ACvQ60gah3ECjWssPhE5fZmqlCk1Je4iIlJhr32XC3Y//PPyi22rX1BAvqlaGRGRilLiLiIiZTbto2Til2WSbRiYdjinTxCYsC3Yn7P2pxbpmxjgy7rgeuxNc9A0UCepioiUl5aDFBGRMpk85wDffnSI9rl5nJOdS8N8WP/lPjBgd/16rAxvSJbNSqbdwn+6tGBJx+bsDguh2Qwny3cU1HT4IiK1lmbcRUSkTDYu2Y+Xvz+5FtfcT1hePtne9dzbfwsL5rewYNcFmI5f5s0wuHihE/OR6o5YRE5nWg6y9DTjLiIiZZJv9yLfanUl5YZBvtVKs7x815rtx/P0Zqw3aBGRcqvSxH3atGn06NGDJUuWeNweExNDz549SUhIcLd9//33/Otf/2LQoEH07t2bG2+8kddff50jR45UZagiIlJKeV72Ym2GYYBxNHE3TXA4Xf+eqKD4SaqDF+RgPJ2N8UwOYa/mkJnnYT8REanaxH3MmDFEREQwadIkkpKSimxbvHgxq1evJjo6msjISAA+/PBD7r//frKyshg1ahQPPPAA3bp1Y+7cudxxxx3k5uZWZbgiIlIKeR7aTMMgNMdVv24cyYHkHMjIK5685ziK3G33ejaf/mOAYQEMkjIh4KWcqglcRE5LpmGU+namq9Iad7vdTmxsLFFRUcTFxfHmm29iGAaJiYlMmTKFDh06cMcdd7j7z58/nwYNGvDOO+/g7e0NwA033IC/vz/vv/8+v/76K7169arKkEVEzkgHM51k5DppHWKDNz+HR+dAei4mwIBOGBefBS1CuW1pCC1Sckhs2ZTCt1ATKDAMgrLzOexlwywsmcl1gI8T7NZj9e52AyM2C5sFpl9psDXFOHqEowwD0wmf/JXHdZ28qvU5EBE53VX5yamRkZFER0czc+ZMFi1axJAhQ4iLi6OgoIC4uDhstmMhZGZmEhQU5E7aCzVo0AAAHx+fqg5XRKTyzF4Bb33hykvHDoLbLy79vvHfwvSvwADGXQ6jBhzbtvB/8PpnkFsAd1zs2p6cDg/Oho9Xu8pU2oWBxXD93KcT/L0HVm+GtGxMp8nWJs3oMeIp0uy+YJp4F+Qxe+kMwjevLfJVrAGYK/7ij43ZvHnutaxv05hLD26jzfa9/NEqjL31fNnu602Aw4GP08TqMHHPqft7uZJ2OFbb7mMDEwoMGL0CsDmh4LjE/egM/fULHbQIzeXVS+0M6ajTsUREoJpWlYmOjmbVqlVMnTqVXbt2sX79emJiYoiIiCjS7/zzz+fzzz9n8uTJXHfddfj4+PD7778zZ84cLrjgArp3714d4YqIVNx//gdRU4/dX7MFfLxgaJ9T77tgFURPO3Z/9RbwscMtvWHZr3DrpGPbfv3HlaAv+hGW/36sfd22Yz+v317k8AbQNmkvkQf2sKZZBBgGb3w9j1s2r/UYTmJgA8bccC/Zdh/swHdd2nHu5l180zgYx9GVZTLsVhrm5nPB3hR+aRJInpfNFdeJDAOOb/aygtMBzuOTd9dtdyrc/FEBXwy1cVkbJe8idZVTJTClVi2Ju81mIzY2lttuu42FCxfSuXNnRo4cWazfgw8+SE5ODgsWLGDevHnu9htvvJGHHnoIi0Uv3CJSS7y33EPbitIl7vEl7HtLb9e/J5r+JWzYUabwLJg0zUgBwOp0cNsfP5TYd2nkhWTbi37jubF1U675Zzf+eflsbBjMxkahHPSy0TU9mxZZeey3WjAKTNJ9ip/IWozVOCFxN48l96bJexudStxFRKjG5SD9/f3x8nLVK/bq1QurtfjV87y9vWnRogUDBgwgNjaWl156iRtvvJFPPvmEZ599trpCLZXk5OQiJ8tmZGSQnp7uvp+Xl8fhw4eL7LNv376T3k9KSsI87kQujaExNEYtHsPHQ322j71UYxTYPbw0+3oV/fc4uTbD8+x2KTkNg1xryfM4Dkvx12srJn0TkzjnwGGi/txKv137sDmdbPK2syPXiXkgkyYHM7AXODwc8QQnnsBqAscN6Xs09z/t/881hsaoZWNI7WOYpqf1uiqXaZrExMSwbt06WrZsSWJiIvPnz6d169buPk6nkzFjxuB0Onn33XddS4sd9fbbbzNjxgxeffVV+vXrV9XhiohU3PLfYVCcq8YcwGKBr56Ci7udet9vfoPLngHn0X2tFvh6Agzo6iq56f045B+9AqlhwEcPw+KfYP6qUoeXY7Vxzu1Ps6lhcwCeXrmYJ39c6rHvtuAmDB3yOHm2Y7Pn3fbtp/u+A+77Kd52JnXvTEawL/gdN8t+qq/AHU7XSjNFLtSE+3xVm5eFn0bZ6BGmGXeRumrCFb+Uum/c5+dVYSSnv2p5Jfzggw9Ys2YNo0ePZuLEiRiGQWxsLA7HsZmYDRs2sGHDBgYOHFgkaQe49NJLAVi71nP9pYjIaWdgV1j5DIzs77qtfKZ0STvAJWfBd0+79hs1wLXvgK6ubee3gx+eg+iLYXhfV0J/XU+IvxtejYKuLaFDUxjZz7X/rb1d2+6/Bnq0hSA/zHre7Dq3C4fDGruO6XTyVJ8buPvi4TgpssYLJtD6SBKvL32LxkcOYc/N54KtuzjruKQdwLfAQUaAN9Tzcl+YyWPSbpqQ74CcAsjMg7Q819ruBsduDhNMk4FtLPw8yq6kXaSOMzFKfTvTVXmNe2JiIlOnTqVjx45ERUVhs9kYO3YsU6ZMYe7cuURFRQFw4IDrTaCgoKDYMQrbjk/0RUROexd1dN3Ko08n182T89q5bsez2+CBwa7bKRhAe2B/sS03um6pGWTHfkDG3J/ZG9KIBrOiOe+CcL6wWZj40nZ2bM7EpOg5pn+EhmD1snDKV2kTmng5uTbSwltX+mJ5KgucuJaOLJxpNwyyJ/jgY1PCLiJyvCp9VXQ6ne6Z9djYWPfSj8OHD6dbt27MnDmTbdtcKx+0adMGgC+//LJY8r50qevr286dO1dluCIiAhDkj+9rt9Pw0AzO2vwMzXq3xnI0iX74kdaEks/i9q3ZGeDPIR9vfmzamO+aNyHi4LH6We98B/65xSdiMGDf//ky/SpvDMOgS9Ojb0MmFE73dwlDSbuIiAdVOuM+f/58NmzYwPjx42nbtq273WKxMGHCBIYNG0ZsbCzx8fG0b9+egQMHsnz5cm677TauuOIKfH19Wb16Nd999x2RkZEMGjSoKsMVEZFS8DcM7FYbiyKPva53OpJKUgMvDKeTHntTaHUkCwuQ7Gvn+/AG5NiLn+AK8Ps4Xx77NpeXvnfN1T870MK/++qaHSJnEl0RtfSq7OTU7du3M2LECNq3b88777zjcRWZefPmMXnyZO666y6io6MpKChg6dKlLFmyhMTERDIzMwkLC6N///6MHj2aevXqVUWoIiJSBlMv+C/bm7Ziv7cXyXY7Ifn5nJe4kycu703DrDx6Jh4p0n9XkC8/tQx13XE6MR/WFVFF5Jgnr/y11H2fWXZuFUZy+quyGffWrVvzww8lrwsMMGLECEaMGHEsGJuNG264gRtuuKGqwhIRkQra5+NN49TDEBRK49w8/HOyMApyyLFZaZiZW6y/u800i14lVUREykRFhCIiUibXvXA+WbmZXPbbj1z+2480O7CT/7briMNqIdW7+AWXUr3tNDyUDvkOdt6ltx0RKco0jFLfznTVcuVUERGpO86/MIj6swYw5ZVEduVZuSjSiyUj61Nvvsm2kHq0TM0iNDsfgFyrhY1hQTTee5gDLwbXcOQiIrWbEncRESmz9u19mTbz2JKUqTszACiwWvimbSMaZ+ZidzhJ8vehwDAYsO8g0KKGohURqRuUuIuISIUFhfsD+a46dsNgv//RlWFME//0bA4H+tdofCIidYESdxERqRQjw3L4aW0uwQUODtfzwmpCo4wc9nnZ+CcspKbDE5HTlGrXS09nCYmISKWYPSKAoPwC/PMdhKdk0zw1Gy+HSYAJTg/XYhIRkbJR4i4iIpUmw1b8mh3pvna8fDSjJiJSUSqVERGRSpNjFpBpt1Iv33Ul1HQvG4n1vIi/Qom7iHhm6uWh1JS4i4hIpfnwnmAuficbfL0wLQYZVgtnpaUwtEeTmg5NRKTWU6mMiIhUmnMifNjxmC+XJ2/g/KRtrLgil19fVNIuIlIZNOMuIiKVys/XyoDOfwJwQZfbazgaEZG6Q4m7iIiIiNQYp5aDLDWVyoiIiIiI1AJK3EVEREREagGVyoiIiIhIjdGVU0tPM+4iIlLpch0WHkq7Ae8pcNlCXTZVRKQyKHEXEZFK9fk/cE/mKNKoj4nBV7vBeKUA0zRrOjQRkVpNibuIiFSqa/8LUPyr74sWOKo9FhE5/ZmGUerbmU6Ju4iIVJpDWU4oYWL9p32acRcRqQgl7iIiUml+O+CEEmfFlLiLiFSEEncREak0+7OAEmvZ9TW3iEhFaDlIERGpNPW9KTLjbnGaBGfnkeZjJ99qYJomhupUReQ4unJq6WnGXUREKs2Jk+1Oi8Hhet6cuyeZ4Kw8Fm3SCaoiIuWlGXcREak0vx049nPj9CwG/LOP0MwctoYG0jzVSvSX3tzSqebiExGpzZS4i4hIpQmwu/71zSsg+pe/8ct3zbA3ycgmoWEOv4fVr7ngROS0ZKpSptRUKiMiIpXGbgVMk8gDR9xJe6H2B1Pxyc2rmcBEROoAJe4iIlJp/kkBDAOHpfjbi9MwcHpoFxGR0inzK+i0adPo0aMHS5Ys8bg9JiaGnj17kpCQAMDPP//M888/z8iRI7nwwgvp0aMHa9euLfH4pmmycOFCbr75Znr16sVll13Gc889R0pKSllDFRGRavbKOicAmxrVJ8XHq8i2hEb1cdqUuIuIlFeZX0HHjBlDREQEkyZNIikpqci2xYsXs3r1aqKjo4mMjATgiy++YOnSpTgcDlq3bn3K40+ZMoWXX36ZZs2a8dBDDzF48GCWLVvGv/71L7Kzs8saroiIVJO0XBMcgGmSb7Mys2ckP4Q3JqFhEP/t0JxlHZozzDe1psMUkdOMiVHq25muzIm73W4nNjaW3Nxc4uLiMI+u/ZWYmMiUKVPo0KEDd9xxh7v/XXfdxapVq5g/fz5XXHHFSY+9fft2FixYQN++fXnttde4/vrriYmJ4emnn+aff/5h/vz5ZQ1XRESqyZq9R2vaXZPupPt48UVkC+Z3j2B1y0ak+3gx55AfadlaElJEpDzKtapMZGQk0dHRzJw5k0WLFjFkyBDi4uIoKCggLi4Om+3YYRs1alTq43755Zc4nU6GDx9epP3iiy+madOmfP7554wePbo8IYuIVI9Xl8CMryA3H8KCwc8bLuwA4y6D5g1Kd4xDaa5j7DgAV50LrRrBnW/CpkTX9vCG8NB1EDUQHp0LL38CzuMWUPexA07IKSlBLpyzcbpbnIATOwX4kYsv+dioTxJWCjCxkEYj8rHhxE4OQdgoIAsbyUZ9svyt+Gc5CXZCw0eHczDA37WguwGYrlu7/cncuGknXk4nz17j4JydO0kLrE89HwuB9W10f6ALLS4OK8MT7cGqP+E//4P69WDMINfzVk7OHYdxzPgfpGZjHdoDS5+IisUmIlIJyr0cZHR0NKtWrWLq1Kns2rWL9evXExMTQ0RE+V/c/vzzTywWC127di22rWvXrnz55ZdkZGTg7+9f7jFERKrM2LdgxtfH7u865Pp3xR/wzjew/lVoGnLyY2RkwwWPwj9HSxHf+eZYAlzor0S4/Q2IXw6r/ip+jJz8UwR64gFdqbyFfGykYuAkkHT3l9IGToJIIpGupBPs3seXTFpbdpCU3hIDCxtbNOJQcIDrQ4QTsBjuzwheNgs+TideDic+GKTXq0eu00J+hknOgQyWj/6BvlN70vrqFqeIvQTvfw/DJx+7AtSMr2DdKxBe9uTduf0Qeee+BEeyAHBM/x/292/Hesu55YtNRE5KV04tvXKfJWSz2YiNjcXhcLBw4UI6d+7MyJEjKxTMgQMHqF+/Pl5eXsW2Fc7cHzhwoNg2EZHTwnvflbztQCq8+82pj/HBj8eS9kKm564ek/ZScXL8bPuJvI5L2gsV4EU69Yu0ZVMPm8NKEK669bcvPhfTMMBqAZvhStwN1+3PJiGsaNvUve+ORo2oV5ALhkGet2vx9w2T/izn4wFe/KjoZVuTM2D6V+U6lGPG/9xJOwCmScGL5TuWiEhlqtDp/f7+/u4ku1evXlit1goFk5OTg91u97jN29vb3ed0kJycTG5urvt+RkYG6enp7vt5eXkcPny4yD779u076f2kpCT3OQMaQ2NojFo4RsEpardTMk85Rm5S8smPUSlK+iRQsny8wcOJYfnYseNam/1AUL1jGzzMoP3TIMj9c6aXDavp+vBgHu2bm1F0jfcy/X+kZBYPOiWzXP/njkPpFJPiWhyhzv7uaowzcgypfQzz+P/1MjBNk5iYGNatW0fLli1JTExk/vz5J105Zu7cuUyZMoXp06fTo0ePYttvueUWjhw5wldfFZ/ZmDJlCnPnzmXRokW0adOmPCGLiFStCx6B1Vs8b7NY4KcX4Px2Jz/GjgMQOd5VI38qIf6umeUys+AuPvcgDz/sZBVJ050YbOZCnMdVWBo4acE29tKcXPyYefG5vHF5zxJH7b7nEDf/9g8FhoE1J4WAHAsWh4k9Jw/v3Hy6/Ks95z7arRyPB1et/0sfH7tvGPDd09C3c5kP5fhuM/kDphRps/57EPbnry1fbCJyUvfeVPpvD6cs7lSFkZz+yj3j/sEHH7BmzRpGjx7NxIkTMQzDXTpTXo0aNSIlJYW8vOJX1isskSnLya4iItVqeZzrRFTL0TIRmxV8vaBzC1j4wKmTdnCdUPnZ467jhAXD6EvgkeuPXpL0KAO4/GzY9hY0DS7xUCUzOHH23Ak4sJNNfTJoxGFa4MQ4mt4b5BBMMPux4vpAYaWAUPaz1bs5O30bkoeVESt+o+WBI56HNE28U7NZ0aIxy8Mb0iw5G0u+A1tePj6mgzbXtyx/0g7wzFDX89SiAXQNh3n3litpB7D2b499fhRG16bQIhjro4OwxV1d/thERCpJuU5OTUxMZOrUqXTs2JGoqChsNhtjx451z4pHRUWVK5hOnTrx008/8fvvv3PuuUVPAvr9999p2bKlTkwVkdOXnw/8+ELFj3NxN9fteC/e5rnvnndPfqzcoxMhGTmuDxGGAb7ex7bvTwG7BUtwAAA++Q587VZX7fmOZBw/bcWra3N8OzbB12LQCMj/bS/5OXnk53px1kVNcDpMDJuFz9dmcOkzv/NuIw+z7obB6o7NaZySyYaH/GlSvxQfYsrCbnM9RyU9T2VkHXYe1mHnVcqxREQqS5ln3J1Op3tmPTY21r304/Dhw+nWrRszZ85k27Zt5Qpm0KBBGIZRbL325cuXs3fvXq688spyHVdE5Izl7eW6hQa6Plgcn7QDNK4PIYHuk0gNL5u7Pt2rVQi+Q8/H2qUphtWCYRgYhoFX92bUu6A19fs1w7BZsXrbsFgthLXyZ1mXDiXHYhgUWAya1C/3gmYiIme0Mr96zp8/nw0bNjB+/Hjatm3rbrdYLEyYMIFhw4YRGxtLfHw8VquVLVu2sHLlSgA2btwIwLJly9iwYQMAV111FWFhrrV727Zty6233sr777/P/fffT79+/dizZw8LFiygdevWDBs2rKKPV0REqsg5jS3sCwksuYNpUlCus6pEpC4ztRxkqZUpcd++fTvTp0+na9eujBgxotj28PBwxo0bx+TJk5k9ezbR0dEkJCQwffr0Iv2WLl3q/rl79+7uxB3g/vvvp1mzZixevJiXXnqJwMBArrzySmJiYvDz8yvr4xMRkWo0sJmF5ftK2Gia1MsrxUm3IiLiUblXlRERETnR8CUFLChhYR1Mk/Z7D/P3a02qNSYROb3dM2RTqfu+/kHHKozk9FehddxFRESO1z+cohdCOp5hcMTPp1rjEZHTn9Mo/e1Mp8RdREQqzcYDeLz4EgCmWb6lzEREBFDiLiIilSi5+GU4XI7OwvcMdlZfMCIidYwSdxERqTTdQkveZsl3MPPOoOoLRkRqBdMwSn070ylxFxGRSnNdSddVMgycFmgYZC2hg4iInIoSdxERqTR/JVPyyakWveWIiFSEXkVFRKTS+JR09qlpgkP17SIiFaET/EVEpNL0bW4FCjxuG9ZBc0UiUpwT1a6Xll5FRUSk0tTzMlyrQZ5YLuN0Mv8GrxqJSUSkrlDiLiIilSr3Xggy0sDpBNPE7nCQdb++4BURqSi9koqISKWbGPghALfffjt2u3cNRyMipzMt81h6mnEXEREREakFlLiLiIiIiNQCStxFRERERGoB1biLiIiISI1xqsS91DTjLiIilWpvmsmm7EY4Tb0bi4hUJs24i4hIpTBNE98Xssm12cG4CrKc/PV5Hq8Pttd0aCIidYJm3EVEpFJcMCuHXLsdLAYYBtitTE3wwjzxYkwiIsdxGkapb2c6Je4iIlIp1hy2uhL24xnwf8uyayYgEZE6RqUyIiJSYW0fP8jAwzm0TU7niK8X37dqzP4AXzAMvtlZ09GJiNQNStxFRKRCcrIKOGtPGh0PpQHQMCuXlimZTO/ZgSwvG/9kOWo4QhE5nenKqaWnUhkREamQX5bsoMPRpB0gw25jS4NAmqVmApBletdUaCIidYpm3EVEpEI2Of1wGhlkeFlZ1q4Z20IDTqh112yaiEhlUOIuIiIVYlgM1jYL4ZewUNK97cVPUBURkUqhxF1ERCrEu6U/y9t4Q4EJNlVgikjZ6MqppadXWBERqZCmuVmuH7ysJ+2X7zDJK9Ca7iIi5aUZdxERqRAzI49eCbv5sVN48Y05BeBtxfp8Ns48wATDCmmPeOPvrbkjEZGyqNJXzWnTptGjRw+WLFnicXtMTAw9e/YkISGhSPtXX33FmDFj6N+/PxdddBE33HADL730UlWGKiIi5ZSelkfE3kPYCwqKbnCa7ll4Z7YJBU5wODHzTYKez6qBSEXkdGRilPp2pqvSxH3MmDFEREQwadIkkpKSimxbvHgxq1evJjo6msjISHf7c889x+OPP05AQADjxo3joYce4rLLLuPgwYNVGaqIiJTTujf/xtsnh3lz5oF5tBTGNMFiuG6F9w3DfeKq06E3YBGRsqrSUhm73U5sbCxRUVHExcXx5ptvYhgGiYmJTJkyhQ4dOnDHHXe4+y9dupSPP/6YJ554guuuu64qQxMRkTJ4bNSfZGUcu3/RZYHcOLo5Ky/5iK+7ncujy7/k3V4XHOvgNCErz5W4e1vB4XQl7RbDncAv/+EIc177m9C0ZAIcuYws2EyLKbdg796q2h+fiEhtUOU17pGRkURHRzNz5kwWLVrEkCFDiIuLo6CggLi4OGw2VwimaTJr1izat2/vTtozMzPx8/PD0NJiIiI15r4b/yzW9sOXafzw+R9c/3sSKWd5M71PL77q2PFYB6vFVRqTc/SqqVbANFwz715WGman03Lwi7yXnAiAAwOnYbC/zxZmd+nHiM5Own3z4IpzoH9n+Gyda9+re4Cfhws6ffYr3DkN8h0QNRA6t4DLz4Ymwcf6ZOXCf9e6PjhENIF126B7azi3bSU+WyJSVk7leaVWLSenRkdHs2rVKqZOncquXbtYv349MTExREREuPvs3LmTxMREhgwZwnvvvcf8+fM5cuQIvr6+9O/fnwceeIDg4OCTjCIiIpXt0P7ckjdaLHzZ4wIGr99GfL8uRbel5x5L2gGbw8G967+h64Hd/K9pOwIduUQcTdoBrJhYTZPmGckMT/iJe9rEsOTdJ+GNz8HbDrn5ro7NQuD756B142NjDXkZFv907P4rR8+r8vGCjx+Gy8+B7fuhz+OwJ7n447jnKphyR/F2EZHTTLWc0m+z2YiNjcXhcLBw4UI6d+7MyJEji/TZsWMHAN988w3x8fEMHz6cl19+mWuvvZYvvviCf/3rX+Tk5FRHuCIictSrD2896fYsPx9CHSbt9h6XEOc7ICO/SD97voPnVy5m1J8/8vbXs7n3ly9LPGZ4ShL5PoEs7tjb1ZB73LH2JMNzi4/d/2t30aT9eDl58H+zXT8/u9hz0g7w+mewKdHzNhGR00i1rcXl7++Pl5cXAL169cJqLbreb1aWa4WBI0eO8NJLLxEVFcWAAQN48MEHiY6OZtu2bXz22WfVFe4pJScnk5t7bCYqIyOD9PR09/28vDwOHz5cZJ99+/ad9H5SUhKmeWyNY42hMTSGxqjpMbIzOKXDwQFc8PfuYw35zmJ9su1e7AoMdd9vnn6kxOMZgNWRzy/NO3rucDTJzsjIIHv9yT9YmAmJRfYp0dF+p/v/h8bQGJU5htQ+hnn8/3oVMU2TmJgY1q1bR8uWLUlMTGT+/Pm0bt3a3efbb7/lkUceoWHDhnz++edF9t+zZw/XXnstl156KS+88EJVhysiIke9/eI//PnLyb/tbLF7Pz+1bMCHF3RyNeQ74FB2kT4NstK4b/3XZNq9uTVhDd0OJfJj0w702vt3sePlWu1ce/sk/vXDf7h+0w/FB3xyCDw91PXzgRRoHF1ycBd3g29i4ckFrll3T7ztsHsmNAw66eMUkaoxYuT2UvedN6f1qTvVYdUy4/7BBx+wZs0aRo8ezcSJEzEMw106U6hxY1e9YoMGDYrtX9iWlpZWHeGKiMhRUf938jdJv6wcmu09SPPk416f7Vbwtx/XyyTPZuOJ3jfyQs+rOXfEU0w55xJmd+vPVbc+xd+hLSicQcqz2Hjs8rsITjvItZt+cK1C06nFsaUkr+8Jj95w7NCN6sPs8Z6DO7s1zBzr+vnfN7r2LVzZxtf1DTANAl37K2kXkVqgyk9OTUxMZOrUqXTs2JGoqChsNhtjx45lypQpzJ07l6ioKAAiIiLw9vZm//79xY5RuAZ8SEhIVYcrIiLHsdsttO1k45+/Cjxuv2XlEhLqd6Dz7oN0TDzIpuYNXRsCvMHP7lpZJjWHNG8/9z4FVhsP9buFW3fuIMM3iME3PUKDnCy6Hd5HHkDbEN5/JAz+/Qq0DYMAX9fMugk0rl88iJED4Jbe8O43kJIBYy+H9GwIb3Ssj583fPQI7E9xJe4h/rDzIDQLdc24i4jUAlWauDudTvfMemxsrHvpx+HDh7NixQpmzpxJ3759adOmDT4+PlxyySV89tlnfP3111x66aXu4yxcuBCA3r17V2W4IiLiwfhnOrBxdQrxL+/BNKFBmI1HX2uHzW7h8Be38e3jG/DKshLz5S/cN+oyCmxHz2GyWo7dnEXr3vOtVuZ8eC4/LNzKr6v3ccHIcNoF+BHYOQxr4UWbwo6brGlU/+RBetvhriuO3Q8J8Nzv+MS/TZNSPX4RqVpOrQZZalVa4z537lymTJnC+PHjGTVqVJFtO3fuZNiwYbRt25b4+HisVisHDhxg1KhRpKSkcNNNNxEeHs6vv/7K119/Tc+ePXn99deLndQqIiI16+mrlrMiOIx9Derzd+GM+/Gy8yH1hGUlvayYz5eQXIvIGWXYqNLXuC+YrRr3KrF9+3amT59O165dGTFiRLHt4eHhjBs3jr/++ovZs13LdTVq1Ij4+HgGDRrEl19+ySuvvMKmTZu48847ee2115S0i4ichvJHdudAUJDnpB1cV071sbmWiwHwshJQv1ouIyIiUqdUy6oyIiJSd81dsIc7djUg3+ZhcuXoW8xVzZ189o+rqVtj+O1OD1c/FZEzkmbcS09THiIiUiFBOEquUTUMME3+O1SJuoh45kRF7qVVbRdgEhGRuikhMY8ue/eduqOIiFSIEncREamQsJY+NEtJqekwRETqPJXKiIhIhXjnOUjx8yvWbpgmpmFgdRQAWitdRDwzDZXKlJZm3EVEpELqNa6HtyOnWHvhm3Gz5APVHZKISJ2kxF1ERCrk0ktCaZiageWEiywVXnTpYMNGHvYSEZGyUuIuIiIV4mU1aNKjOc0PJ2MUJu9OJ1gsYJpYDF2DQ0RK5jRKfzvTKXEXEZEKm/JQS3b5B2Jajr6tFP6b7+Ses/VuKyJSGZS4i4hIpXjmIiuk57pm200TsvMhp4DnL9aJqSIilUGJu4iIVIonBnpzTYQFMvIgJQccTjaMUZmMiEhl0XKQIiJSaZbe5kt+fj7x8fEAdGp8ew1HJCKnO6eWgyw1zbiLiIiIiNQCStxFRERERGoBlcqIiIiISI1xolKZ0tKMu4iIiIhILaDEXURERESkFlCpjIiIVIqk9ALCZhTeux1/stCaMiIilUcz7iIiUimOJe0GYJBh+hHxjlmDEYlIbeAwSn870ylxFxGRSnJCkm4Y7MrUO62ISGVR4i4iIpVDk+siIlVKNe4iIlJ1lMyLyCnoyqmlpxl3EREREZFaQIm7iIiIiEgtoFIZERGpsL4LCjy2W5zOao5ERGobpyplSk2Ju4iIVMgNHxXw/V7guDrViN37uGHtb+TbbFyY0I7/vdMaq1Vf8oqIVIQSdxERqZCPtxW932frPyx6ZzbeDgcAB39ZzXnGCNbNalsD0YmI1B2a/hARkUr14Dcr3Ek7QMPsLG7ctJG756XVYFQiIrWfEncREalUz1w+iNtH3MqvLZq72xqlZzBtq77kFZHinBilvp3pypy4T5s2jR49erBkyRKP22NiYujZsycJCQkA/Pzzzzz//POMHDmSCy+8kB49erB27VqP+/7+++88/PDDXHfddfTt25d+/fpxyy238Pbbb5ORkVHWUEVEpKqZJkEZRWfS17ZqyZKzunLNuNFsbtgAgM86dwKbhQe/zq2JKEVE6oQyJ+5jxowhIiKCSZMmkZSUVGTb4sWLWb16NdHR0URGRgLwxRdfsHTpUhwOB61btz7psXfu3ElOTg5XXHEF9957L+PHj6dTp0688847REdHk5OTU9ZwRUSkCr01ZCGp9QI8bsux23m3V08mDLqUJV27AAZzvzlSvQGKiNQhhmmaZb6uXUJCAlFRUZx99tm8+eabGIZBYmIiQ4cOpWXLlsyePRubzfWV6IEDB6hfvz5eXl7MnTuXKVOmMH36dHr06FHq8WbPns3UqVN59tlnufzyy8saroiIlEdyOnyyBi5sBx1bwrYkyMkHuxW8bGzNsvHWgyuYc94AbE4H3vl57AxtDMe9rdhzHeQf9/W2kVeAb76Da1sUsGBgNvy+A649H7zscDjddWvfFI5kwIFU18+6qqJInXbpmL2l7vv1zKZVGMnpr1wFh5GRkURHRzNz5kwWLVrEkCFDiIuLo6CggLi4OHfSDtCoUaMKB9msWTMA0tJ0YpOISLUYPQ3e/fbYfR+7K2kHdgeG8OCg4by99G1ezcvh5WULABMLsKJNJ24adj/J/oFgmgTn5NHlUDo/NAkm12oBh0mW1cqSROg0xcH3s2cQmv8aXHEOfLkB8gqgYSCkZrl+7tAMPnwIOresiWdBROS0Uu6TUwvLYaZOncqrr77K+vXr3WU0FZWTk0NKSgpJSUmsXLmSqVOnYrPZ6NmzZ4WPLSIip/Db9qJJO7iTdoD7rhjJC9/+h8A8V/mi5WjSDjBg218889VCKHBdeOmAvw+pNiudkzPAYWJigGmSZbfTMimV226819X307WuRB3gYNqxn//eA3dMq8pHKyJSa5T7FH+bzUZsbCy33XYbCxcupHPnzowcObJSgpo+fTrz5s1z32/VqhWvvvoq4eHhlXJ8ERE5iY9Xn3TzyladCE85BICT4jNA/bdvcm1wmmA3+L1BAP32HIF8VzJvBRxOE1u+k9XN2pw6ntVbIDcfvO1lfigicvrTlVNLr0LLQfr7++Pl5QVAr169sFqtlRLUDTfcwLRp03jxxRcZPnw4fn5+pKenV8qxK0tycjK5ucdWR8jIyCgSY15eHocPHy6yz759+056PykpieNPOdAYGkNjaIwaGePCDpxM54OJJPv6A5DQqFmx7X80OrYMJA6TfCyk2I+bJzIA0yTTy07rox8ATioiDLztp+dzpTE0Ri0eQ2qfcp2cCmCaJjExMaxbt46WLVuSmJjI/PnzT7pyTHlPTv3pp58YP368Tk4VEakuZz8AG3Z43LSmWVve7HExs5bM5OV+g7kqYT1d9u8GYJ9/fQbc/gR/NzyW0NscTnA4XdUzVsBioeOWRHa2bsJPM5+g28Hd0LYx/LPftYPVAg7X7Dw+Xq4a9yvPrbrHKiI16uJ/lf7k1G9n6OTUcvnggw9Ys2YNY8eO5ZJLLmH48OHExsYya9asSpt5L3ThhRcSGhrK4sWLlbiLiFSH9ZPggx9g1nLo1QHuvgK+2QjbD3B+4yA6GF4sGfA4zo9+p/v9E7l0y0Z8s3P5ok03sr28ixyqwGoBqwWLaeI0Tay5+eQ2rMf+iPX4jzoHxj4KbcNg1Z+w9wgMOgs2JcKuQ3BJN2gYVENPgojI6aVciXtiYiJTp06lY8eOREVFYbPZGDt2LFOmTGHu3LlERUVVcpiQm5urVWVERKrTkItct+PvHxUEXA/838pdtEo+wBeRZ7uWgSwwwXH0i9wTvs91Gga+OVlsecCPZg2aAk2Bvsc69O187OeLOsJFiMgZwKEropZamWvcnU4nsbGxOBwOYmNj3Us/Dh8+nG7dujFz5ky2bdtWrmAOHfJc6/jf//6XjIwMunTpUq7jiohI1Xj1P1fzT2hj1x3DALsFfKyufz244ux6NGvgU40RiojUHWWecZ8/fz4bNmxg/PjxtG3b1t1usViYMGECw4YNIzY2lvj4eKxWK1u2bGHlypUAbNy4EYBly5axYcMGAK666irCwsIAuPfeewkKCqJbt240adKEjIwMNmzYwMqVK2ncuDFjxoyp6OMVEZHK5GUHS0HxdgsYHF3+sZABHw7xLt5XRERKpUyJ+/bt25k+fTpdu3ZlxIgRxbaHh4czbtw4Jk+ezOzZs4mOjiYhIYHp06cX6bd06VL3z927d3cn7tdddx3Lly/nk08+ISUlBZvNRvPmzRk1ahQjRoygfv365XiIIiJSE67f/AuhaemsbdqGncGN6GhJA1rVdFgicppxqFKm1Mq9qoyIiAiA8YqHGfejem3+kwKCSbfl8debFb9An4jUPX3G7jt1p6O+nx5WhZGc/iq0jruIiEjESRZ9+altR7LMAubFNKq+gERE6igl7iIiUiFb7rQRaMe1qswJTIvB72+34ZzOgdUfmIhIHaPEXUREKiz1XptrVZkTqXZVRE7BaRilvp3plLiLiEjl0ClTIiJVSom7iIhUHeXyIiKVplxXThURERERqQwOlcCUmmbcRURERERqASXuIiJSOU4sizFNvAzVyoiIVBYl7iIiUikWXG2A03SdpGqagJP0e/QVuIicXEEZbmc61biLiEilGNrJxtBO8P3OfP63bBGh1kwM4/aaDktEpM7QjLuIiFSqC5pCqDWzpsMQEalzlLiLiIiIiNQCKpURERERkRqj5SBLTzPuIiIiIiK1gBJ3EREREZFaQKUyIiJSqd5ZD3elRQEG/5oC93QrYMogvd2IiGcFqpQpNc24i4hIpTFNk7tWgevtxQAMXt8Iq3ZqBWYRkYpS4i4iIpXG8qoDMI5egAn3xZj6fVCjYYmI1An67lJERCqXaULhKhGF/zqcNRePiJzWClCtTGlpxl1ERCqPw3EsWT+OrXAGXkREyk2Ju4iIVDmbQ4m7iEhFKXEXEZEqZzhVKiMiUlGqcRcRkcp1fI37Udk+9hoKRkROd/kqcS81zbiLiEjl8lDjbjgcNRCIiEjdosRdRESqXGhmek2HICJS66lURkREKk3jlMPsD21UrD3dx68GohGR2iDfw7d04plm3EVEpHIYN/Dw9//1uCnXpnkiEZGKqtLEfdq0afTo0YMlS5Z43B4TE0PPnj1JSEgAIDY2lh49eni8LViwoCpDFRGRisjLA+CspN2et2tGTUSkwqp0CmTMmDF8//33TJo0iZ49e9KkSRP3tsWLF7N69WruvPNOIiMji+z39NNPFztWp06dqjJUERGpiE53A9A4I83jZkNXThURqbAqTdztdjuxsbFERUURFxfHm2++iWEYJCYmMmXKFDp06MAdd9xRbL8rr7yyKsMSEZHjFS7f+M162HYA2jeD/l3Kdox/DgGws36Ix81WreMuIiXIr+kAapEqLzqMjIwkOjqamTNnsmjRIoYMGUJcXBwFBQXExcVh81D3aJommZmZ+Pr6YrVaqzpEEZHayTThmQ9g5ldgtUDH5vBXomvbuMvg0RtOXqIybBK8/79Tj3PVOXBOW3j3W7Bb4Z6r4IHBHrsua3+2x3b/nDx2rU6nZc/Gpx5PREQ8qpazhaKjo1m1ahVTp05l165drF+/npiYGCIiIjz279+/P5mZmVitVrp168Ydd9zBBRdcUB2hiojUHm8sgwn/OXZ/16FjPz82H0IDYMwgz/vO+a50STvAZ+tct0L/9x40qQ/D+rruGze4N+V6eb7QUrOULH4b8l9a7hpTujFFRKSYallVxmazERsbi8PhYOHChXTu3JmRI0cW6xcSEsKtt97KI488wiuvvMK4cePYtWsX48eP57PPPquOUEVEao9FP5Z/+2ufVmzshT94bN4TGOqxPcfPm7ZHDlRsTBGpk7IMo9S3M121LQfp7++Pl5cXAL169fJYAnPPPffw4IMPcuWVV9K/f3+ioqL4z3/+Q0hICK+88grZ2dnVFe4pJScnk5ub676fkZFBevqxC4zk5eVx+PDhIvvs27fvpPeTkpIwTVNjaAyNoTFKN0ZoACcV6l/iGPlHt5VXVj1bkcdRqPu+HR77Z3tZybJ7FWmrc/8fGkNj1LIxpPYxTE+vvJXMNE1iYmJYt24dLVu2JDExkfnz59O6detS7T9z5kxmzpzJG2+8oZIZEZFCPybAwAmQ6+HULl8vWPkMnNfO876bdkOne0s/lpcN8gpcP/v7wE8vQJdw1/2Bj8EK17K+WXYv6j03t2htvWmC0+T1hZ8yft2NpR9TRM4I9e8t/QeKlCmev9U7U1RLjfsHH3zAmjVrGDt2LJdccgnDhw8nNjaWWbNmlerk06ZNmwKQkpJSxZGKiNQivSJh3Sswe4Xr5NS+nWDlX65tUQOgQ7OS9+3YAv6cDOc+BDknWdPB1wv+mAxZuTBnpevk1OiLoe2x5X1Z/ry7zv371h2LnxBrGGCYStpFxKNsVcCUWpUn7omJiUydOpWOHTsSFRWFzWZj7NixTJkyhblz5xIVFXXKY+zatQuA0NAz+1OWiEgxnVrAS8edM3T5OWXYtyVkLyx9/4nFz01yqwdkQlj6Ec/bq/7LXRGROq9Ka9ydTqf7pNTY2Fj30o/Dhw+nW7duzJw5k23btgGQnZ1NVlZWsWPs27ePxYsXU79+fbp27VqV4YqISHkNdpUxtko+QJ9tf3nooCk1EZGKqtIZ9/nz57NhwwbGjx9P27Zt3e0Wi4UJEyYwbNgwYmNjiY+PZ9euXYwZM4ZLLrmEVq1aUa9ePbZv387SpUvJycnhhRdewMfHpyrDFRGR8przf/D+EJyGhR/C23vooBl3EZGKqrLEffv27UyfPp2uXbsyYsSIYtvDw8MZN24ckydPZvbs2QwePJi+ffvy22+/8c0335CTk0NwcDC9evVi5MiRdOzYsapCFRGRirJZoXd7nvY/F6fVw1uLSmVEpAR5+kau1KplVRkRETkzGC/kgL144m4UFOB8VN+aikhxxn3Jpe5rTg6pwkhOf9W2jruIiJwBLJ5nzkyL3m5ERCqqWpaDFBERERHxSJUypaYpEBERqXq6VLmISIUpcRcRkcrjLOG0KZ1OJSJSYUrcRUSk8pSUoGvGXUSkwlTjLiIilaeEk1M14y4iJdIH+1LTjLuIiFQ9Je4iIhWmxF1ERCrNzeGmxyS9yZHSr9MsIiKeKXEXEZFKs3CoL7b8/GPJu2ninZPLrmca1mxgIiJ1gBJ3ERGpVFkP2emRn4AtP49zjQxynqiH3cPVVEVEpGz0SioiIpXuzgY/AT9x++2313QoIiJ1hhJ3EREREak5WlWm1FQqIyIiIiJSCyhxFxERERGpBZS4i4iIiIjUAqpxFxGRSrPpYAEjn9+OUdAH0zC59IiT1o1qOioROa2pxL3UlLiLiEil+PSvXMbPOcjOZm3cbT2npfLS9VZu7663GxGRilKpjIiIVIp/z0xkZ4MmRdoOBgTxyMepNRSRiEjdoikQERGpsE2HnRz29/e4Lctmr+ZoRKR2Ua1MaWnGXUREKuyrTXkk1Qv0uC3T26eaoxERqZuUuIuISIUNCs7Gx5GPX25u8Y26uIqISKVQ4i4iIhXWft9+6uXn8+SXXxTbZjidNRCRiNQaRhluZzjVuIuISIUZzYLosWsX479fhZfDwTsXXoiJwR0//8RnHTsBnWs6RBGRWk+Ju4iIVJilYxh51sMA/OvHH/jXjz+4t30dEcm3F0/j4m9jaio8EZE6QYm7iIhUilUREXzUtRvbQ0P5vFMnztqzhwt2bOd/7dpy6x+r+HtzCh3a16/pMEVEai3DNE2zpoMQEZHaz/pSLk7DAMtxp08dfYtpceQQw8OyeWF8mxL2FpEzlfFw6a/1YE4MqsJITn86OVVERCqF1VFQNGkH14oyhkGSfxDpVt+aCUxEpI4oc+I+bdo0evTowZIlSzxuj4mJoWfPniQkJJCbm8vHH3/MAw88wDXXXMNFF13Etddey2OPPcb27ds97m+aJgsXLuTmm2+mV69eXHbZZTz33HOkpKSUNVQREakmptOJ0zj5W8q/f1teTdGIiNRNZU7cx4wZQ0REBJMmTSIpKanItsWLF7N69Wqio6OJjIxk37597qT7mmuu4aGHHmLQoEH8/PPPDBs2jLVr1xY7/pQpU3j55Zdp1qwZDz30EIMHD2bZsmX861//Ijs7u/yPVEREqs5viThsJZ82VWC14RdgrcaARKT20HqQpVWuGveEhASioqI4++yzefPNNzEMg8TERIYOHUrLli2ZPXs2NpuNlJQUkpKSiIyMLLL/tm3bGD58OO3atWPOnDnu9u3bt3PLLbfQu3dvJk2a5G7/9ttveeSRRxg7diyjR4+uwMMVEZGq8NuKXXT/tWnJHRwmSzYvZfC7N1ZfUCJSKxgPp5W6rznR8xWazxTlWlUmMjKS6OhoZs6cyaJFixgyZAhxcXEUFBQQFxeH7eisS/369alfv36x/du0aUObNm3YunVrkfYvv/wSp9PJ8OHDi7RffPHFNG3alM8//1yJu8jpLC8fXv8Mlv8BHZvBg9dCWEhNR1V7rdkCU5dBZg6M7A/X9fTcb9dBeGUJbNwJ2QVwOA0shuvE0N2HId8BXjboFg4Tb4N+neGzXyHmXUg8DCZgWMBRADg4NrNVOK9T+LN59GfL0Xu2o/fzibRYWN+4Gefc/xLmiVdKNU0woeFXazGN/wAmBidelMkCxdqO8rFD02Bo0QBsVtdj2p8Cgb4Qc6Xr+fllK5zfDv5vMAT6leVZFhGpNcq9HGR0dDSrVq1i6tSp7Nq1i/Xr1xMTE0NERMQp93U6nRw+fJjg4OAi7X/++ScWi4WuXbsW26dr1658+eWXZGRk4O/vX96wRaQqRU+D+atcP3++Dj5ZA39OAR+vmo2rNlr3D/R5HPIKXPc/Xg3vjYdRA4r2S8+GXv+GPckc+xrZwxepOU5Y8w8MmABTR8Pd8RRNlJ1H9z++nMU42l6YtFtwJe1OTHwBCwZZGJh4O52EpacVT9oBTJPHvv2CCxP/oeSvus2jYzs8xJ4P2w64bsdLzYJH5x67/8V6WP47fP9cCWOIiNRu5V5VxmazERsbi8PhYOHChXTu3JmRI0eWat/Fixdz6NAhrr766iLtBw4coH79+nh5FX+Tb9SokbuPiJyGDqbC+/8r2rZtPyxbVzPx1HYzvjqWtBeauqx4v49+Pi5pP1n959Hk2wSe/gCPyb3HthNn3sGVYFsAB4Z7hh4aZ6Qy6O/fPBzCYPSaH1jdrMPRBk8z68dm8yvkf5tg/baKHUNEqpdK3EutQq+Q/v7+7iS7V69eWK2nPvFow4YNTJ48mYiICG6//fYi23JycrDb7R738/b2dvc5HSQnJ5Obm+u+n5GRQXp6uvt+Xl4ehw8fLrLPvn37Tno/KSmJ40850Bgao1aNUeAAp4eELK+gdj2O02WMfA8zz0cT+SLHzC8o3u8UnAUejl0mJZ8a9ewXi4o3GgYrItqTa62mEpbjnrta9X+uMTRGNY8htU+5L8BkmiYxMTGsW7eOli1bkpiYyPz582ndunWJ+2zatIlx48YRGBjIzJkzadKkSZHtt9xyC0eOHOGrr74qtu+UKVOYO3cuixYtok0bXcBD5LR09XOu2ulCjevD1mngr/W7y+z7v6D/U0U/DE25A+65qmi/w+nQLgaOZHDSUhn3bLYTnr4FnlrsoZ/hoa1w/MIZcetxpTLG0VKZwn0h3duHRhNmkGMv+s3p8mmTMA0nA7evO3qsEz88FJbpOEqIv5S6hcOGSa7140WkVjAeKcPJqS+d2SenlnvG/YMPPmDNmjWMHj2aiRMnYhiGu3TGk4SEBGJiYqhXrx5vvfVWsaQdXOUwKSkp5OXlFdtWWCJTWDIjIqeh9x9wnRx4dmsY1gdWPaukvbz6dIJP/w2XdIMLO8Bb/yqetAOEBsCqZ2BIL+jQFFo3hpBACA10bbNaAAMsVmjbCBY+AE/eDLPGQoj/0QTXcJ2c6k6oC+vaC/89/mRVBwYmBtlAPib2o0U4TvYEBDPj/IHFCmEMh5MOB/YTnHn4aNJfUmJeQtJuGFDfD9qHQZeW0DDQdcJq/Xpw9xVw56Wu37kxl8KXTylpF6l1VCtTWuWacS9c+rFVq1bEx8djs9mYO3cuU6ZM4e677yYqKqpI/8Kk3cfHhxkzZtC8eXOPx33rrbd49913mTFjBueee26RbYMHD8Zms/HRRx+VNVwREaliyZkFfHjRfMbcOhRsJ8wJOU0iDuxnfkAC50+4pGYCFJHTlvFI+qk7HWW+FFCFkZz+yjzj7nQ63TPrsbGx7qUfhw8fTrdu3Zg5cybbth07Mai0STvAoEGDMAyD+fPnF2lfvnw5e/fu5corryxruCIiUg02/XqQ/7tuMFg9z4iN+WElKTkVKIEREZGyLwc5f/58NmzYwPjx42nbtq273WKxMGHCBIYNG0ZsbCzx8fEcOHCAmJgY0tLSuOWWW9i4cSMbN24scrwBAwbg6+v6Kr1t27bceuutvP/++9x///3069ePPXv2sGDBAlq3bs2wYcMq+HBFRKQqhNnyGPDPNpae5WGteYvBofoBdCCj+gMTkdOfKmBKrUylMtu3b2fEiBG0b9+ed955x+MqMvPmzWPy5MncdddddOvWjbFjx570mEuXLqVp02NX23M6nSxatIjFixezZ88eAgMD6du3LzExMR4v5iQiIjXPLHDwbWQcl459yuP2TvsSeSNoGwOeGljNkYnI6c54tAylMi+e2aUy5V5VRkRExC0phV0dHiL8iTc9nhw6+sdvueaJixh8ji6gJyJFKXEvvQpe6UJERAT4bQcv9rve84oupsn20EZcc3a96o9LRKQOKXONu4iISDHdW7GyXYjnbQ4HN43tiqFlGkXEE700lJpm3EVEpMLWOvxplFbCRVSsVsaeq3kiEZGKUuIuIiIVtutQPoHZ2eDptCnniZdkEhGR8lDiLiIiFXZt1m4+7dzFY417QF5uDUQkIrWHrpxaWkrcRUSkwqwNAjAtnt9S8qwqkxERqQxK3EVEpMIcbZrgXZBf02GIiNRpStxFRKTCrBaDsPRkj9uapR6p5mhEROomJe4iIlIpFoxvhuFwFG00TQZe2LBmAhKR2kEl7qWmxF1ERCrFhc2s7PyXFRwOLA4Htvx8nuvp5O3rvGo6NBGROkFnDImISKVpUd9G3gMm8fHxANx+4e01HJGISN2hxF1EREREao6uqlxqKpUREREREakFlLiLiIiIiNQCStxFRERERGoBJe4iIlKpvtsFD6XdxAcZ3Ws6FBGROkUnp4qISKUxXik4+lMg3zjPwWuKiflgjYYkIlJnaMZdREQqxV9JhUn78StEGPi6k3kREakIzbiLiEil6DzPc3tO9YYhIrWNVoMsNc24i4iIiIjUAkrcRURERERqAZXKiIiIiEgNUq1MaWnGXURERESkFlDiLiIiIiJSC6hURkRERERqjiplSk0z7iIiIiIitYASdxERERGRWqDMifu0adPo0aMHS5Ys8bg9JiaGnj17kpCQAMDPP//M888/z8iRI7nwwgvp0aMHa9eu9bhvQkICkydPZvjw4QwYMIABAwYwcuRIPvjgAwoKdOU9EZFayTRrOgIRkTqhzIn7mDFjiIiIYNKkSSQlJRXZtnjxYlavXk10dDSRkZEAfPHFFyxduhSHw0Hr1q1PeuzZs2ezdOlS2rdvz1133cXYsWMJCgripZde4r777sPUi7+IiIhI3WKU4XaGM8xyZMMJCQlERUVx9tln8+abb2IYBomJiQwdOpSWLVsye/ZsbDbXea8HDhygfv36eHl5MXfuXKZMmcL06dPp0aNHseNu2LCByMhIfHx8irQ/+eSTfP7550yePJnevXuX86GKiEhVMl4p4ZtR08R8yF69wYhIrWE8lVXqvubTflUYyemvXDXukZGRREdH88svv7Bo0SKcTidxcXEUFBQQFxfnTtoBGjVqhJeXV6mO271792JJO8All1wCwNatW8sTroiIlMenv8BVz8Lg56H7A9BwFFzzHCSnu7bvT4HoN+Dyp+GONxj4+1q8CvKKHMKrIJ+QjDQwbgDrjfD219X/OERE6ohyLwcZHR3NqlWrmDp1Krt27WL9+vXExMQQERFRmfEBcPDgQQCCg4Mr/dgiIuLBve/A68uKt//3V2gSDb++Aj0egrxjs+wL/dbQMPZdrvnzF2xOJ6FZGdz3/Wf45eW6OjhNGPMW/LIFZt5VTQ9ERE5/qoEprXIn7jabjdjYWG677TYWLlxI586dGTlyZGXGBkBWVhZz5szB39+f/v37V/rxRUTkBKmZMO2LkrfnO2DwC0WSdoCXBlyLYZq8t/BNQrIzS97/7W+UuIuIlEOFloP09/d3l8H06tULq9VaKUEVcjgcPP744+zdu5dHHnmEoKCgSj1+RSQnJ5Obm+u+n5GRQXp6uvt+Xl4ehw8fLrLPvn37Tno/KSmpyAm4GkNjaAyNUSNjHEwDh5OTcR5MLda2LaQRVtNJQG7OSfettsehMTSGxjjpGFL7lOvkVADTNImJiWHdunW0bNmSxMRE5s+ff9KVY051curxCuvmP/vsM8aOHcvo0aPLE6aIiJSVaUJYNOwvnpy7RQ2A91YUaVrctSdDRv4fC+ZPZuiGH93tuVYb3o7jZuf9vSH9/cqOWkRqKeOp7FL3NZ/2rcJITn/lnnH/4IMPWLNmDaNHj2bixIkYhkFsbCwOh6PCQZmmyTPPPMNnn31GdHS0knYRkepkGPBNHDSp73n78L4QPx5uvMDV96izErfTc+dmxtz4L6ZfcAl7AoP5uWUE10Q9dNyxge1vVWn4IlLLaDnIUitXjXtiYiJTp06lY8eOREVFYbPZGDt2LFOmTGHu3LlERUWVO6DCpP3TTz/l9ttv5667VAcpIlLturSEfbMgIxu87a7SmawcCA44lqwvfhgKCiA7DwL8aP9iNnanE9NiYdyNYxh349FjmSb88Cy0bgxhoTX2kEREarsyz7g7nU73zHpsbKx76cfhw4fTrVs3Zs6cybZt28oVjGmaPPvssyxdupSoqChiYmLKdRwREakk/r5gt4GPF4QEFplhB8Bmg4Cj6yrb7OR7eVNg87Bme69OStpFRCqozDPu8+fPZ8OGDYwfP562bdu62y0WCxMmTGDYsGHExsYSHx+P1Wply5YtrFy5EoCNGzcCsGzZMjZs2ADAVVddRVhYGABTpkxhyZIltG/fnjZt2rBsWdGlyJo3b063bt3K9UBFRERERGqzMiXu27dvZ/r06XTt2pURI0YU2x4eHs64ceOYPHkys2fPJjo6moSEBKZPn16k39KlS90/d+/e3Z24//XXXwBs3ryZp556qtjxr776aiXuIiK1TfnWQBARkROUe1UZERGR4xmvFHje4HRiPly6K2iLyJnHmFCGVWXizuxVZcp9ASYREZFSsVTokiEiUtdptZhS06upiIiIiEgtoMRdRERERKQWUOIuIiIiIlILqMZdRERERGrOideHkBJpxl1EREREpBZQ4i4iIiIiUgsocRcRkUpxUcOajkBEpG5T4i4iIpXif6M8nTZlMqVvtYciIlInKXEXEZFKs/xGANN9O7ch3HO+1kEQEakMejUVEZFKM6C1jbx784mPjwfg9mG313BEInLa06IypaYZdxERERGRWkCJu4iIiIhILaDEXURERESkFlCNu4iIiIjUIBW5l5Zm3EVEpNKtzg7ncK53TYchIlKnGKZpmjUdhIiI1A2Br+STbgKGAaYJpon5sFdNhyUipzHj6dxS9zWfOrMnBDTjLiIilcadtHP0X4sFY2J+jcYkIqc5owy3M5wSdxERqRThr+QfS9qL0Be7IiKVQYm7iIhUil3Kz0VEqpQSdxERqRweZ9tP0i4iImWixF1ERKqW1kAQEakUStxFRKSSKEEXEalKStxFRERERGoBXTlVRERERGqOToMpNc24i4iIiIjUAkrcRUSkcqjEXUSkSpW5VGbatGnEx8fz5JNPcu211xbbHhMTw9q1a5k9ezatW7dm2bJlfP/992zZsoXk5GQaNGhA586dufPOO2ndunWRfbOyspg3bx6bNm0iISGBgwcPcs455zBz5szyP0IREakeJvrKW0SkCpV5xn3MmDFEREQwadIkkpKSimxbvHgxq1evJjo6msjISPbt28dzzz1HSkoK11xzDQ899BCDBg3i559/ZtiwYaxdu7bI/ikpKcycOZO//vqL9u3bY7VaK/boRESk+liUtYuIVCXDNMu+wG5CQgJRUVGcffbZvPnmmxiGQWJiIkOHDqVly5bMnj0bm81GSkoKSUlJREZGFtl/27ZtDB8+nHbt2jFnzhx3e15eHkeOHKFx48YA9OnTh44dO2rGXUSkFjBeKShxm/mg1kIQEc+MZ3NL3dd8wrsKIzn9lavGPTIykujoaH755RcWLVqE0+kkLi6OgoIC4uLisNlcL9D169cvlrQDtGnThjZt2rB169Yi7V5eXu6kXUREaoHNe6D3v+GBWTUdiYhInVfuKZDo6GhWrVrF1KlT2bVrF+vXrycmJoaIiIhT7ut0Ojl8+DDBwcHlHV5ERGraNc/Df4+WPP7wN5df1ZAv+l0OhkpmRKQM9JpRauVeVcZmsxEbG4vD4WDhwoV07tyZkSNHlmrfxYsXc+jQIa6++uryDi8iIjXtv0XPU8ry9/P8Blz2ikwREfGgQstB+vv74+XlBUCvXr1KdTLphg0bmDx5MhEREdx+++0VGb5GJScnk5t7rCYrIyOD9PR09/28vDwOHz5cZJ99+/ad9H5SUhLHn3KgMTSGxtAYp+0Y27ZxonMSdxdrO+0fh8bQGGfwGFL7lOvkVADTNImJiWHdunW0bNmSxMRE5s+fX2yJx+Nt2rSJcePGERgYyMyZM2nSpMlJx9DJqSIipzHjhiJ3b7pqOB/2G1x81t00MR+yV2NgIlKbGM/nlbqv+ZhXFUZy+iv3jPsHH3zAmjVrGD16NBMnTsQwDHfpjCcJCQnExMRQr1493nrrrVMm7SIicpob3KPI3d2BDTyXyqh+VUSkUpQrcU9MTGTq1Kl07NiRqKgoWrVqxdixY/nzzz+ZO3dusf6FSbuvry8zZsygWbNmFQ5cRERq2JLHYOub0KcTPHQta865qKYjEhGp08qcuDudTvfMemxsrHvpx+HDh9OtWzdmzpzJtuNqHwuTdh8fH2bMmEHz5s0rL3oREalZbZvAqmdh4qiajkREpM4r83KQ8+fPZ8OGDYwfP562bdu62y0WCxMmTGDYsGHExsYSHx/PgQMHiImJIS0tjVtuuYWNGzeycePGIscbMGAAvr6+7vsLFy50n0xRUFBAUlIS77zzDgBhYWFcddVV5XqgIiIiIiK1WZkS9+3btzN9+nS6du3KiBEjim0PDw9n3LhxTJ48mdmzZ9OtWzdSU1MBSjzBdOnSpUUS93nz5hU5c3rv3r1Mnz4dgHPOOUeJu4iIiIickcq9qoyIiMjxjFcKStxmPlju6/2JSB2nVWVKT6+kIiJStZzOmo5ARE5nWniq1Cp0ASYRERE3p77AFRGpSkrcRUSkcqjyUkSkSqlURkREKoe+7haRctGLR2lpxl1ERCqHRW8pIiJVSa+yIiJStZTQi4hUCr2aioiIiIjUAkrcRUSkUrSo6QBEpHYyynA7wylxFxGRSrFLF1kSEalSStxFRKTSBAFQuCykCZi6aqqISCVR4i4iIpUm5UEbeffCGPuXTPSZR969NR2RiEjdoWkQERGpdOf67qnpEERE6hzNuIuIiIiI1AKacRcRERGRmqPVYkpNM+4iIiIiIrWAEncRERERkVpAibuIiIiISC2gxF1EREREpBZQ4i4iIiIiUgsocRcRERERqQW0HKSIiIiI1BwtB1lqmnEXEREREakFlLiLiIiIiNQCStxFRERERGoBJe4iIiIiIrWAEncRERERkVpAibuIiIiISC2g5SBFREREpOYYWg+ytDTjLiIiIiK1WmxsLP7+/jUdRpVT4i4iIiIiUguoVEZEREREao4qZUpNM+4iIiIiUqf98ccfXH755fj7+xMYGMi1117L1q1b3dvvuOMO+vbt675/5MgRLBYL55xzjrstOzsbb29v5s2bV62xH0+Ju4iIiIjUWbt376ZPnz7s37+f2bNn884777B582b69OnDwYMHAejbty9r1qwhJycHgO+//x5vb29+++03UlJSAPjpp5/Iy8srkuBXN5XKlINpmqSnp9d0GCIip6X8/Hyys7MBSEtLw26313BEIuJJQEAAxhmwostrr71GXl4eX331FQ0bNgSgZ8+etGvXjmnTphEbG0vfvn3Jzc3l559/pn///qxatYrBgwfz3Xff8b///Y+rr76aVatWER4eTsuWLWvssShxL4f09HSCgoJqOgwRkdPefffdV9MhiEgJUlNTCQwMrOkwMB+s2nT0+++/Z+DAge6kHSA8PJxevXrx/fffA9C6dWtatGjBypUr3Yl7VFQUTqeTlStXuhP3mpxtByXu5RIQEEBqampNh1GlMjIyuOqqq/jss8/OiOWVapKe6+qj57r66LmuPnquq09de64DAgJqOoRqceTIEbp3716svUmTJvz999/u+3379mXVqlVkZGSwfv16Zs2ahcPhYN68eeTn5/Pzzz8zderUaoy8OCXu5WAYxmnxCbUqWSwWrFYrgYGBdeLF6XSm57r66LmuPnquq4+e6+qj57p2CgkJYf/+/cXak5KSCAkJcd/v27cv9913H9999x1BQUF07twZh8PBAw88wIoVK8jOzq7xGXednCoiIiIidVbv3r359ttvOXz4sLtt9+7d/Pjjj/Tp08fd1rdvX7Kzs3nllVfo06cPhmHQrVs3AgICeP7552nSpAnt2rWriYfgphl3EREREan1HA4HixcvLtZ+7733Eh8fz6BBg3j88cdxOBxMmDCBkJAQYmJi3P0iIyNp1KgRK1euZNKkSYCryqJ37958+umn3HzzzdX2WEqixF088vLy4s4778TLy6umQ6nz9FxXHz3X1UfPdfXRc1199Fyf3nJychgyZEix9vj4eFatWsWDDz7IbbfdhsViYcCAAbz66qtFTlgF16z74sWLi5TE9OvXj08//bTGy2QADNM0zZoOQkRERERETk417iIiIiIitYASdxERERGRWkA17lIqOTk5vPvuu3z++eccPnyYBg0acM011zBmzJiaDq3O2rRpE6NGjcLb29t9gQipHIXr8v7www9s27YNh8NBREQEd955J+eff35Nh1er7dy5k1deeYX169fj6+vLZZddxt13342Pj09Nh1anfPPNN3z++eckJCSQmppK8+bNuemmm7jhhhuwWDQnV5WysrK46aabOHDgAHPmzKFTp041HZKcQZS4yyk5HA7uv/9+9u/fz9ixYwkLCyMpKYmkpKSaDq3OMk2TiRMnEhwcTFZWVk2HU+fk5uYSHx/P1VdfzW233YbNZuPTTz8lJiaGSZMmFVkeTEovPT2dcePG0aRJEyZOnEhycjKvvfYaqampPPPMMzUdXp0yb948wsLCuOeeewgNDWXt2rW8/PLL7Nmzh3vvvbemw6vT3nnnHRwOR02HIWcoJe5ySkuWLCEhIYHFixcTGhpa0+GcEZYuXUpKSgqDBw/mP//5T02HU+d4e3uzdOnSIhdSu+CCC9i1axfz5s1T4l5OH374IWlpaSxYsID69esDYLPZeOKJJ4iOjqZ169Y1G2Ad8tprrxEcHOy+36NHD7Kysli0aBHjxo3TqidVZMeOHXzwwQfcd999vPDCCzUdjpyB9H2anNKSJUu45JJLlLRXk/T0dN544w0eeOABbDZ9tq4KhVc+PJ5hGLRv356DBw/WUFS1348//sj555/vTtoBBg4ciJeXFz/88EPNBVYHHZ+0F+rQoQO5ubmkpaXVQERnhpdffpkbb7yR8PDwmg5FzlBK3OWk8vPzSUhIoHHjxjz55JNcdNFF9OvXj8cff5yUlJSaDq9OevPNN+nYsaNmfauZ0+lk48aNmhWugO3btxd7/ry8vGjevDnbt2+voajOHBs2bCAoKMhjUi8V980337BlyxZGjx5d06HIGUyJu5xUamoqDoeDOXPmkJGRwauvvspDDz3EmjVreOyxx2o6vDrn77//ZunSpTzwwAM1HcoZZ+HChezcuZPhw4fXdCi1VlpaGgEBAcXaAwICNAtcxf766y+WLl3K0KFDsVqtNR1OnZOTk8Nrr71GTEwM/v7+NR2OnMH0PfwZKCMjg0OHDp2yX9OmTXE6nYDrjXfixInY7XYA/Pz8ePjhh/njjz/o0qVLlcZbm5Xlubbb7UycOJGbbrqJVq1aVX1wdUxZnusT639//fVXXn/9dUaMGME555xTVSGesXSdv6p16NAhHn74YTp37kxUVFRNh1Mnvfvuu4SGhnLNNdfUdChyhlPifgZasWIFcXFxp+w3f/58dx1ft27d3Ek7wHnnnQfAtm3blLifRFme6x07drB9+3aeffZZ0tPTAcjLywNcde9eXl54e3tXaby1WVme6w4dOrjvb9myhf/7v/+jf//+3HPPPVUZYp0XGBjo/t09XkZGhkqQqkhGRgb33HMPPj4+TJo0SefFVIF9+/Yxb948Xn75ZTIzMwHIzs4GXEtDZmVl4efnV5MhyhlEf+FnoGuuuaZMswZhYWElbtN6wSdXluf6u+++Iy0tzWP/AQMGMGrUKMaPH1/ZIdYZZf29BkhMTOTuu+8mMjKSp59+GsMwqii6M0Pr1q2L1bLn5eWRmJjI4MGDayiquis3N5cHHniA5ORk4uPji5wULJVnz5495Ofnc9999xXbNnbsWLp06cJ7771X7XHJmUmJu5xS7969WblyJXl5ee4Sg9WrVwPQrl27mgytTrnmmms499xzi7T997//5euvv2bKlCk0adKkhiKrmw4dOkRMTAyhoaG88sorRb5RkvLp1asX7777LikpKe4kcsWKFeTl5XHRRRfVbHB1TEFBAY8++iibN2/m7bffPukEi1RMhw4dmD59epG2zZs3M2nSJP7973/TuXPnGopMzkRK3OWURo4cyeeff86DDz7ILbfcwsGDB3njjTfo379/kZIDqZimTZvStGnTIm2//vorFouFHj161FBUdVNOTg733HMPR44c4f777y82S9y1a9caiqx2u/HGG1m0aBH/93//x+jRo90XYLriiitUKlPJXnrpJb7//nvuuececnJy+P33393bWrdurRMoK1FAQECJr8EdO3YkMjKymiOSM5kSdzmlsLAw3nrrLV577TUefvhhfHx8GDhwoMevDUVqg+TkZDZv3gzAgw8+WGz72rVrqzukOiEgIIC33nqLl19+mYceeggfHx8uu+wylXhVgZ9//hmA119/vdi26dOn68O+SB1lmDrdX0RERETktKczC0VEREREagEl7iIiIiIitYASdxERERGRWkCJu4iIiIhILaDEXURERESkFlDiLiIiIiJSCyhxFxERERGpBZS4i4iIiIjUAkrcReSkYmNjMQyDHTt21HQoHDhwgKCgIGbOnOlu27FjB4ZhEBsbW3OByWmjVatW9O/fv9z79+/fn1atWlVaPHXF3XffTceOHSkoKKjpUETOaErc5Yx04MABHn74Ybp06UJAQABBQUG0a9eOW2+9lY8++qhI3/79++Pj41PisV555RUMw+C7777zuD01NRU/Pz8Mw+C9994r8TitWrXCMAz3zcvLi1atWjF69Gh2795dnodZ5zz55JOEhIRw++2313Qo1SY2NpZPPvmkpsOQarRhwwZiY2Or/cPyd999R2xsLCkpKcW2PfbYY+zYsYPp06dXa0wiUpQSdznj7N69m27dujFt2jR69erFiy++yPPPP8/VV1/NunXrmDVrVqWOt2DBAnJycmjbti3vvvvuSfuGhYUxd+5c5s6dy5QpU+jZsyezZs2iZ8+eHDp0qFLjqm327NnDrFmziImJwW63u9vDw8PJzs7miSeeqMHoqk5cXJwS9zPMhg0biIuLq5HEPS4uzmPi3rRpU2655Raef/55zbqL1CBbTQcgUt1efvll9u/fz9KlS7nmmmuKbHvttddITEys1PHeffdd+vbtyy233MJdd93F33//TYcOHTz2DQwMZMSIEe7748aNo1GjRrzxxhvMmjWLhx9+uFJjq01mzpyJaZoMHz68SLthGCf9RkREKsdtt93G7Nmz+eSTT7jppptqOhyRM5Jm3OWMs3nzZgAGDBjgcXvz5s0rbayNGzfy66+/EhUVxdChQ/H29i7zjP5ll10GwD///FNin88//xzDMJg0aZLH7X369CE0NJS8vDwA1qxZQ1RUFO3bt8fPz4+AgAAuuugiPv7441LFFBUVhWEYHrcZhkFUVFSx9oULF9K7d28CAgLw8/OjZ8+eLF68uFTjASxatIju3bsTFhZWpN1TjfvxbYX7+fr6EhERQXx8PAC7du3ipptuIiQkhICAAIYNG0ZqaqrHx3nw4EFGjhxJaGgofn5+DBw4kF9//bVYjG+++SaDBg2iWbNmeHl5ERYWxogRI0qcOV2xYgVXXXUVoaGh+Pj40KZNG+644w4OHTrEd999536OZ8+e7S6hKk399eHDh7nnnnto2bIlXl5eNG3alNGjR7Nv374i/QrHeO+993jnnXfo1KkT3t7ehIeHM3HixFOOA5X3XAP88ccf3HjjjTRo0ABvb286dOjA008/TW5ubrG+mzZt4qqrrsLf35/69etz7bXXsm3bthLj/Oabbxg0aBD169fHx8eHbt26VUrZR3x8PD169HD/HQ0YMICvvvqqWL+S/i7ee++9IqV2UVFR7lKwAQMGuP/fC3+/C885+fPPP7nnnnto0qQJPj4+nH/++Xz99ddFjn2y8z9OPHelf//+xMXFAdC6dWv3uMeX9/Xv35969eqxcOHCsj1JIlJpNOMuZ5w2bdoA8Pbbb3PfffeVmICeqKRSlaysrBL3eeedd6hXrx433XQT/v7+DB48mDlz5vDcc89hs5Xuz2/Lli0ANGjQoMQ+gwYNIiwsjDlz5vDAAw8U2bZ9+3Z++OEHxo0bh5eXFwAff/wxmzdvZujQoTRv3pzDhw8ze/ZsbrjhBubPn8+wYcNKFVtpPfHEEzz33HNcfvnlPPPMM1itVj7++GOGDBnCG2+8QUxMzEn3P3DgAAkJCdx1111lGve///0vM2bMYNy4cYSEhDBr1iyio6Ox2+088cQTXHzxxTz//PP88ssvzJo1Cx8fH48frC6//HJCQkKIjY0lKSmJN954g379+vHjjz/SrVs3d79XX32VXr16cemll1K/fn3++OMP3nnnHZYvX87vv/9OaGiou29hXC1atOCuu+6iZcuW7Nq1i08//ZTExEQ6duzI3Llzue222+jTpw9jxowBwN/f/6SPOS0tjd69e/P3338zatQozj//fP744w9mzJjBV199xS+//ELjxo2L7PPWW29x4MABRo8eTVBQEPPmzeORRx6hefPmpf5dqOhzvW7dOvr27YvFYiEmJobmzZvz5ZdfMmHCBH766Sc+++wzLBbXXNP27dvp3bs3WVlZ3HXXXbRp04Zvv/2WAQMGePx7nDlzJmPHjuWCCy7g8ccfx9/fn6+//ppx48bxzz//8PLLL5fqMZ7oscce44UXXuDcc8/lmWeeIScnh3fffZfLL7+cuXPnFvt2qDT+9a9/4e3tzcyZM3nsscfo2LEjQJHfM4CRI0ditVp55JFHSE9PZ8aMGVxxxRUsW7aMQYMGlXncxx9/nJCQED7++GNee+019+tNr1693H2sVivnnXceK1euxDTNUr92ikglMkXOMP/8848ZGBhoAmaLFi3MYcOGma+99pq5du1aj/379etnAqe8rVixosh+OTk5ZkhIiDly5Eh322effWYC5pIlS4qNEx4ebkZERJgHDx40Dx48aG7bts2cNWuWGRQUZFqtVvO333476eN68MEHTaBYv9jYWBMwV69e7W7LyMgotn9mZqbZvn17s2PHjkXaJ0yYYALm9u3b3W2jRo0yS3r5AMxRo0a5769du9YEzEcffbRY32uvvdYMCAgw09LSTvrYli9fbgLmq6++Wmzb9u3bTcCcMGFCsbZ69eqZu3btcrcfPHjQ9PHxMQ3DMCdPnlzkONdff71ps9nM9PT0Yo/z+uuvN51OZ5HHZBiGeckllxQ5hqfn9ZtvvjEB86WXXnK37d692/Ty8jI7depkpqamFtvH4XC4fz7x+TyVxx9/3ASKPb558+aZgHnnnXe621asWGECZlhYmHnkyBF3e2ZmptmgQQPzggsuOOV4lfVcX3TRRabFYjF//fXXIn3vvPNOEzDnz5/vbhs6dKgJmJ9//nmRvjExMSZg9uvXz922d+9e09vb27z11luLxX7PPfeYFovF3Lp1q7utX79+Znh4+Ckf999//20ahmH27NnTzMnJcbcfOnTIbNKkiRkcHFzk96Gk/8f4+Phirx+e2goV/j2ef/75Zm5urrt99+7dZr169cx27dq5f1c9/W2ceJzj/649tZ3ojjvuMAEzKSmpxD4iUnVUKiNnnDZt2vDbb79x11134XQ6WbBgAffffz89evSgW7duHksg7HY7X3/9tcdb4UzoiT7++GOSk5OLfD1+2WWXERYWVuJJqlu3bqVhw4Y0bNiQNm3aEB0dTXBwMB9++GGxGbcTjRo1CoA5c+YUaZ83bx6RkZGcf/757rZ69eq5f87KyuLw4cNkZWUxcOBANm3aRFpa2knHKosFCxYArhnCQ4cOFbkNHjyY9PR0fvrpp5Me4+DBgwCEhISUaezrrruOFi1auO83aNCA9u3bY7FYGDt2bJG+ffr0oaCgwGNZy8MPP1xkdvHcc8/l0ksvZfny5UWeq8Ln1el0kpqayqFDhzjrrLMICgpi9erV7n4ffPABeXl5PPnkkwQGBhYbr3BmuTw+/vhjQkJCin07MWzYMCIiIjyWQ91+++3Ur1/ffd/Pz48LLrjA/W1PaVTkuT548CA//PADV111Feecc06Rvk8++SSAe7Unp9PJp59+yllnncXll19epO9jjz1WLK7FixeTm5vL7bffXuz375prrsHpdPLtt9+W+nEWWrJkCaZp8vDDD+Pt7e1uDw0N5a677uLIkSOsWLGizMctrfvvv9/9DRq4SvyGDx/Oli1b+PPPP6ts3MJvjQ4cOFBlY4hIyVQqI2ekVq1aMW3aNKZNm8a+ffv46aefmD17NkuXLuXqq6/mzz//LJIkWiwWLrnkEo/H2rBhg8f2d999l4YNG9K8eXO2bt3qbr/00ktZsGABSUlJNGnSpMg+LVq0cJcPFNZIR0RElOor6S5dunD22WezYMECXnrpJaxWKz/88ANbt27lhRdeKNL3wIEDPPHEEyxZssTjG3BKSorHhLI8Nm3aBECnTp1K7LN///6THqPw8ZumWaaxW7duXawtODiYsLCwIslWYTu46sNPVFiucLxOnTrx1VdfsX37ds466ywAli9fztNPP83q1avJyckp0v/IkSPunwsT4sL9KtO2bdvo3r17kZV3wPUcdu7cmSVLlpCWllbk/7ewfOx4oaGhHp+LklTkuS6sTe/cuXOxY7Ro0YKgoCB3nwMHDpCRkeHx/6Rp06YEBQUVaSv8/Ss8V8STU/3+eXKymLt27VqkT1Uo6XcSXOfDdOnSpUrGLfwbVJmMSM1Q4i5nvLCwMG644QZuuOEGhg0bxvvvv8+yZcuKrO5SVjt27ODbb7/FNE3at2/vsc/s2bN55JFHirT5+fmV+AGhNEaNGsV9993H119/zeWXX86cOXOwWCxFHovT6eTSSy8lISGBe+65h/POO4+goCCsVivx8fEsWLAAp9N50nFKetP2tExc4Rv9smXLiiWThTwlP8dr2LAhUDT5LQ2r1Vqmdij9h4MTE5g1a9YwaNAgIiIiePHFF2ndujW+vr4YhsGtt95a5Dkt6weQylLSuCd7PkqrIs91eZ6P0iaOhceOj48v8cRzTx9cSnvcsm47UXmXVvT0+E/8nTzZc1TecZOTk4Fjf5MiUr2UuIsc58ILL+T9999nz549FTpOfHw8pmkyY8YMj+UdTz/9NLNmzSqWuFfUsGHDeOihh5gzZw4DBgxg0aJFDBw4sEjC8vvvv7Nx40aeeuop9yoShd55551SjVP4mJKTk4s8Pk8zjO3bt+eLL76gefPm7pnIsurcuTOGYRT55qI6bdq0iQsuuKBYm8Vica/y8v777+NwOPj888+LzD5nZmYW+8BRuBzohg0bPM6cVkSbNm3YvHkz+fn5xT4o/fXXXzRo0KDSvk2pLG3btgXwWOKRmJhIamqqu0+jRo3w9/fnr7/+KtZ37969xVarKfzgHBoaWqEPxSeL+cTlXQsfR2EfcP3NFCa9x/P0N1OaDyV//fVXsfK5wm8XCj+IHP93WlnjFpbzNWrU6JR9RaTyqcZdzjgrVqwgOzu7WHth7SycvKzjVJxOJ++99x6dOnVizJgx3HTTTcVuw4cPZ/Pmzfzvf/8r9zieNGzYkCuuuIJPPvmE+fPnk5KS4q59L1Q4A3rirOAff/xR6uUgC5Ohb775pkj7q6++Wqxv4Wz/Y4895nGWrzS1sg0bNqRTp06sWbOmVPFVtokTJxZ5vtatW8c333zDwIED3UlwSc/r888/X+wbjJtuugkvLy+effZZj+cTHH8Mf3//Mn3TcP3115OcnMyMGTOKtP/nP/9h69at3HDDDaU+VnVp2LAhF110EcuWLStWevbcc88BuOO2WCwMHjyY3377jS+++KJI3+eff77YsYcMGYK3tzexsbEeV5xJTU31uNzkqVx33XUYhsErr7ziXmYVXEnym2++SXBwMP3793e3t2/fnp9++qlIDEeOHHEvmXm8wpWDTvb//tprrxUZNzExkQULFtC+fXv3N1gBAQE0adKE5cuXF/md2rZtm8eLep1qXIfDwdq1a+nbt69KZURqiGbc5Yzz6quv8sMPP3D11Vdz7rnnEhQURFJSEh9++CG//vorAwYM4Kqrrir38b/++mt27drFU089VWKfG2+8kUcffZR3332X3r17l3ssT0aNGsXSpUu5//778ff3L5aodezYkc6dOzNx4kSysrLo0KEDmzdvZsaMGXTp0oV169adcoyhQ4fy2GOPMWbMGBISEggNDeXzzz/3uGTmeeedR1xcHBMmTKB79+7cfPPNNG3alH379vHrr7+ybNmyIglISYYMGcIzzzzDvn37iq3lXtV27tzJZZddxuDBg9m3bx9vvPEGvr6+RT6oXH/99bz22mtceeWVjBkzBi8vL77++ms2btxYbCnP5s2bM3nyZGJiYujatSsjR44kPDycPXv2sGTJEmbNmkX37t0B6NmzJ9988w0vv/wyLVq0oF69esUuHHa8hx9+mMWLF3PPPfewfv16zjvvPPdykM2bN+fpp5+ukueool5//XX69u1Lv379iImJoVmzZnz11VcsXbqUyy67jFtuucXd99lnn+WLL77g+uuvJyYmxr0c5Nq1az0+12+99RajR4+mY8eO7uf64MGD/P7773zyySf89ddfpVof/3jt2rXj0Ucf5YUXXuCiiy5i6NCh7uUgk5KSmDNnTpGTwO+++25GjBjBwIEDue2220hJSeHtt98mPDycpKSkIsfu0aMHFouFF154gSNHjuDn50eXLl2K1K0XFBTQp08fhg4dSnp6OtOnTyc7O5upU6cWSarvvvtunnjiCa644gquu+469u7dy/Tp0+nSpQu//PJLkXF79uwJwL///W/3dSd69uzp/gbpu+++IzMzk5tvvrlMz5WIVKJqXcNG5DTw008/mQ888IDZo0cPs1GjRqbNZjODgoLMCy64wHz11VeLLO1mmq7l4by9vUs83ssvv1xk6bYhQ4aYgLlx48aTxtGtWzezXr167qUQw8PDzQ4dOlTswZmmmZuba4aEhJiAGRUV5bHPjh07zJtuusls0KCB6evra5533nnmRx99VKYl4n7++WezV69epre3txkaGmreeeed5pEjR0pc9u6///2vOWjQIDM4ONj08vIymzdvbl5++eXmm2++WarHtWfPHtNms5mvvPJKkfaTLQfpaRm8kpb787QEX+FykAcOHDBHjBhhhoSEmL6+vuaAAQM8Lh/68ccfm+ecc47p5+dnhoaGmrfccou5c+dOMzw8vMgShYW+/PJL85JLLjEDAwNNb29vs3Xr1ubo0aPNQ4cOufskJCSYAwcONP39/U2gVEsVHjp0yLz77rvN5s2bm3a73WzSpIl5xx13mHv27CnSr3A5yPj4+GLHONmSn8errOfaNE3z999/N6+//nozJCTEtNvtZrt27czY2Nhif5P/387dqygSRGEYrsWfFhrM1DZpA0FMFYw1NhDvQEMFDc1VUFDMjDvwFoykYy9BBAMxNDIWNPg22mDGkd1hZ1aKfZ+4oQ6HLvioPtWStN/vVa/X5bquksmkGo2Gjsfj015vt1s1m02lUinFYjFls1nVajUtFgtdr9ff1vxMEAQql8tKJBJyXVfValWbzebDZ+fzuXzfVzweV7FYVBAET3sRBIEKhYKi0eib/v7aj7vdTr1eT5lMRo7jqFKpKAzDhzXv97sGg4E8z5PjOCqVSlqv10/39WQyke/7ikQiD+9Gq9WS53m63W5/3B8AX+uH9KJbUgDwSZ1Ox4RhaA6Hw9OLrl+p3W6b1Wr1ssukwHvD4dCMRiNzOp0+/ZXgb5zPZ5PP581sNjP9fv+frQvgLWbcAVhjPB6by+Xy4VwwgO8znU5NLpcz3W731aUA/zVm3AFYI51OP/w1BMD3Wy6Xry4BgOHEHQAAALACM+4AAACABThxBwAAACxAcAcAAAAsQHAHAAAALEBwBwAAACxAcAcAAAAsQHAHAAAALEBwBwAAACxAcAcAAAAsQHAHAAAALPAT52SNcb3s4RQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 800x950 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 可视化特征重要性\n",
    "shap.summary_plot(shap_values, train_X, show=False)\n",
    "plt.savefig('特征重要性1.svg', format='svg', dpi=600, bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "6299d2f8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x950 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "shap.summary_plot(shap_values, train_X, plot_type=\"bar\",show=False)\n",
    "plt.savefig('特征重要性2.svg', format='svg', dpi=600, bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4188f714",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "36e126ab",
   "metadata": {},
   "source": [
    "#### 2.7AdaBoost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "dff2fa3f",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import AdaBoostClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "28198380",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = AdaBoostClassifier(algorithm = 'SAMME.R', random_state=0)\n",
    "\n",
    "model.fit(train_X,train_y) \n",
    "score_r = model.score(test_X,test_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "01ebce40",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Label</th>\n",
       "      <th>Precision</th>\n",
       "      <th>Recall</th>\n",
       "      <th>F1</th>\n",
       "      <th>Support</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0.946827</td>\n",
       "      <td>0.873418</td>\n",
       "      <td>0.908642</td>\n",
       "      <td>632</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>0.794702</td>\n",
       "      <td>0.683761</td>\n",
       "      <td>151</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>总体</td>\n",
       "      <td>0.879942</td>\n",
       "      <td>0.858238</td>\n",
       "      <td>0.865274</td>\n",
       "      <td>783</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Label  Precision    Recall        F1  Support\n",
       "0     0   0.946827  0.873418  0.908642      632\n",
       "1     1   0.600000  0.794702  0.683761      151\n",
       "2    总体   0.879942  0.858238  0.865274      783"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_label_list = test_y\n",
    "predict_label_list = model.predict(test_X)\n",
    "eval_model(test_label_list, predict_label_list, labelEncoder.classes_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "5d4a597b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "0.9149761086428032"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predict_pro_list, AUC = get_roc_auc(model,test_X,test_label_list)\n",
    "AUC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cf2f34d9",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "14c90a24",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 统计详情\n",
    "evaluation_now = list(eval_model(test_label_list, predict_label_list, labelEncoder.classes_).iloc[1,[1,2,3]].values)\n",
    "evaluation_now.append(score_r)\n",
    "evaluation_now.append(AUC)\n",
    "\n",
    "evaluation_ls.append(evaluation_now)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3d74fefe",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "23631737",
   "metadata": {},
   "outputs": [],
   "source": [
    "evaluation_data = pd.DataFrame(evaluation_ls)\n",
    "evaluation_data.columns = ['precision','recall','f1','accuracy','AUC']\n",
    "evaluation_data['模型'] = ['LogisticRegression','KNeighborsClassifier','GaussianNB'\n",
    "                         , 'RandomForestClassifier','SVM', 'GradientBoostingClassifier', 'AdaBoostClassifier']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "66fb237c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>precision</th>\n",
       "      <th>recall</th>\n",
       "      <th>f1</th>\n",
       "      <th>accuracy</th>\n",
       "      <th>AUC</th>\n",
       "      <th>模型</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.542857</td>\n",
       "      <td>0.754967</td>\n",
       "      <td>0.631579</td>\n",
       "      <td>0.830140</td>\n",
       "      <td>0.850648</td>\n",
       "      <td>LogisticRegression</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.502304</td>\n",
       "      <td>0.721854</td>\n",
       "      <td>0.592391</td>\n",
       "      <td>0.808429</td>\n",
       "      <td>0.867031</td>\n",
       "      <td>KNeighborsClassifier</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.520000</td>\n",
       "      <td>0.258278</td>\n",
       "      <td>0.345133</td>\n",
       "      <td>0.808429</td>\n",
       "      <td>0.714278</td>\n",
       "      <td>GaussianNB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.880351</td>\n",
       "      <td>0.881226</td>\n",
       "      <td>0.880771</td>\n",
       "      <td>0.881226</td>\n",
       "      <td>0.921117</td>\n",
       "      <td>RandomForestClassifier</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.566502</td>\n",
       "      <td>0.761589</td>\n",
       "      <td>0.649718</td>\n",
       "      <td>0.841635</td>\n",
       "      <td>0.864050</td>\n",
       "      <td>SVM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.897810</td>\n",
       "      <td>0.814570</td>\n",
       "      <td>0.854167</td>\n",
       "      <td>0.946360</td>\n",
       "      <td>0.963828</td>\n",
       "      <td>GradientBoostingClassifier</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.600000</td>\n",
       "      <td>0.794702</td>\n",
       "      <td>0.683761</td>\n",
       "      <td>0.858238</td>\n",
       "      <td>0.914976</td>\n",
       "      <td>AdaBoostClassifier</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   precision    recall        f1  accuracy       AUC  \\\n",
       "0   0.542857  0.754967  0.631579  0.830140  0.850648   \n",
       "1   0.502304  0.721854  0.592391  0.808429  0.867031   \n",
       "2   0.520000  0.258278  0.345133  0.808429  0.714278   \n",
       "3   0.880351  0.881226  0.880771  0.881226  0.921117   \n",
       "4   0.566502  0.761589  0.649718  0.841635  0.864050   \n",
       "5   0.897810  0.814570  0.854167  0.946360  0.963828   \n",
       "6   0.600000  0.794702  0.683761  0.858238  0.914976   \n",
       "\n",
       "                           模型  \n",
       "0          LogisticRegression  \n",
       "1        KNeighborsClassifier  \n",
       "2                  GaussianNB  \n",
       "3      RandomForestClassifier  \n",
       "4                         SVM  \n",
       "5  GradientBoostingClassifier  \n",
       "6          AdaBoostClassifier  "
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "evaluation_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "08a3884a",
   "metadata": {},
   "outputs": [],
   "source": [
    "evaluation_data.to_excel(r'全指标建模结果.xlsx',index = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "85131e75",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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